Next generation PKPD mesoscale modeling and simulation of nanoscale tumor targeted drug delivery particles.¶
Targeted NanoCarrier Model¶
Advantages of Physiological based pharmacokinetic modeling (PBPK):
- Alternative approach to the modeling and simulation of drug concentration data.
- More simpler and logical than the compartmental approach since it elimates imaginary compartments and uses real tissue boundaries and tissue volume.
Equations for PBPK Models:
- Two step process
- Pharmacokinetic: The delivery of the drug to the tissue (controlled by tissue blood flow).
- Pharmacodynamics: Uptake of the drug by the tissue (controlled by the ability of the drug to diffuse through capillary and cell membrane to the tissue).
- The overall rate of distribution is controlled by the slower of these two steps.
- Since most drugs are small molecules that can easily pass throug the cell membrance, the PBPK models for these drugs are mostly perfusion limited distribution models.
Derivation:¶
In Non targeted Nanocarrier model:¶
After the administration of the drug and a pseudo equillibrium is achieved between the tissue and the blood, a ratio is established between tissue concentration to the blood concentration. This ratio is called Partition Coefficient (Kp).
\begin{equation} \Large \mathcal{K}_{p} = \frac{C_{Tissue}}{C_{out}}\\ \Large C_{T} = \mathcal{K}_{p}\ C_{out} \end{equation}
In Targeted Nanocarrier Model:¶
In [1]:
from PIL import Image
basewidth = 800
img = Image.open('targeted.png')
hsize = int((float(img.size[1]) * float(basewidth / float(img.size[0]))))
img = img.resize((basewidth, hsize), Image.ANTIALIAS)
img
Out[1]:
The concentration of NC is enhansed at the endothelial cell boundary therefore
\begin{equation} \normalsize \text{Conc. of bound Nanocarrier at the endothelial cell membrane} \neq \text{Conc. in Blood}\\ \Large C^\star \neq C_{out} \end{equation}
Now, the partitioning of NC conc. in the tissue is given by:
\begin{equation} \Large C_{T} = \mathcal{K}_{p}\ C^\star \end{equation}
For PKPD Model, it is necessary to relate C* to Cout.
Starting with C*, the total number of NCs bound to an endothelial cell is
\begin{equation} \Large N^{bound}\ =\ C^{\star}\ l^{2}_{EC}\ \mathcal{L}_{EC,d} \end{equation}
Where
\begin{equation} \normalsize C^{\star} = \text{NC conc. bound to the endothelial cells.}\\ \normalsize l^{2}_{EC} = \text{Cross Section area of one cell (2)}\\ \normalsize \mathcal{L}_{EC,d} = \text{length of the boundary layer within which the NC concentration is enhansed due to binding (1)} \end{equation}
In [2]:
from PIL import Image
basewidth = 300
img = Image.open('cell.png')
hsize = int((float(img.size[1]) * float(basewidth / float(img.size[0]))))
img = img.resize((basewidth, hsize), Image.ANTIALIAS)
img
Out[2]:
The total number of NC bound to an endothelial cell can also be described as:
\begin{equation} \Large N^{bound}\ =\ C_{out}\ l^{2}_{EC}\ L_{cap}\ P_{b} \end{equation}
Where
\begin{equation} \normalsize L_{cap} = \text{size of the cell free layer in the capillary in which the NC marginates}\\ \normalsize P_{b} = \text{probability of NC binding where}\\ \normalsize K_{EC}\ C_{out} = \frac{P_{b}}{1-P_{b}},\ \ otherwise,\ \ P_{b} = \frac{K_{EC}\ C_{out}}{(1 + K_{EC}\ C_{out})} \end{equation}
When applying the limit
\begin{equation} \normalsize K_{EC}\ C_{out} << 1\\ \normalsize P_{b} \approx K_{EC}\ C_{out} \end{equation}
Substitiuting it back to Nbound, we get:
\begin{equation} \Large N^{bound}\ =\ C_{out}\ l^{2}_{EC}\ L_{cap}\ K_{EC}\ C_{out}\\ \Large N^{bound}\ =\ l^{2}_{EC}\ L_{cap}\ K_{EC}\ C_{out}^{2} \end{equation}
Now comparing both Nbound equations, we get a relationship between C* and Cout:
\begin{equation} \Large C^{\star}\ {\color{red}{l^{2}_{EC}}}\ \mathcal{L}_{EC,d}\ =\ {\color{red}{l^{2}_{EC}}}\ L_{cap}\ K_{EC}\ C_{out}^{2}\\ \Large {\color{blue}{C^{\star}}}\ =\ \frac{{\color{blue}{L_{cap}}}}{{\color{blue}{\mathcal{L}_{EC,d}}}}\ {\color{blue}{K_{EC}\ C_{out}^{2}}} \end{equation}
The above equation describes the bound NC on the endothelial cells but to calculate the total NCs harvested in experimental procedure, we have to account for NCs diffused into the tissue compartment:
\begin{equation} \Large N_{tot}\ =\ N_{T} + N_{EC}\\ \Large N_{tot}\ =\ \mathcal{K}_{p}\ C^{\star} + C^{\star}\ l^{2}_{EC}\ \mathcal{L}_{EC,d} \end{equation}
From the total tissue volume, the volume of endothelial cells are measured as:
\begin{equation} \Large \varphi_{EC}\ =\ \frac{V_{EC}}{V_{T}}\ =\ \frac{l^{2}_{EC}\ D_{EC}}{V_{T}}\\ \end{equation} where D_{EC} = Endothelial cell Diameter
Substituting the equations, we get \begin{equation} \Large V_{T}\ =\ \frac{l^{2}_{EC}\ D_{EC}}{\varphi_{EC}} \end{equation}
Total conc. of NP in tissue:
\begin{equation} \large C_{tot}\ =\ \frac{N_{tot}}{V_{T}} =\ \frac{\mathcal{K}_{p}\ C^{\star} + C^{\star}\ l^{2}_{EC}\ \mathcal{L}_{EC,d}}{\frac{l^{2}_{EC}\ D_{EC}}{\varphi_{EC}}}\\ \large C_{tot}\ =\ \frac{\mathcal{K}_{p}\ C^{\star}\ \varphi_{EC} + C^{\star}\ l^{2}_{EC}\ \mathcal{L}_{EC,d}\ \varphi_{EC}}{l^{2}_{EC}\ D_{EC}}\\ \large C_{tot}\ =\ C^{\star}\ \Bigg(\frac{\mathcal{K}_{p}\ \varphi_{EC} + l^{2}_{EC}\ \mathcal{L}_{EC,d}\ \varphi_{EC}}{l^{2}_{EC}\ D_{EC}}\Bigg)\\ \large C_{tot}\ =\ \frac{L_{cap}}{\mathcal{L}_{EC,d}}\ K_{EC}\ C_{out}^{2} \Bigg(\frac{\mathcal{K}_{p}\ \varphi_{EC} + l^{2}_{EC}\ \mathcal{L}_{EC,d}\ \varphi_{EC}}{l^{2}_{EC}\ D_{EC}}\Bigg)\\ \large C_{tot}\ =\ \frac{L_{cap}}{\mathcal{L}_{EC,d}}\ \Bigg( \frac{K_{EC}\ C_{out}^{2} \mathcal{K}_{p}\ \varphi_{EC} + K_{EC}\ C_{out}^{2}\ l^{2}_{EC}\ \mathcal{L}_{EC,d}\ \varphi_{EC}}{l^{2}_{EC}\ D_{EC}}\Bigg)\\ \large C_{tot}\ =\ \frac{L_{cap}}{\mathcal{L}_{EC,d}}\ \Bigg(\frac{K_{EC}\ C_{out}^{2} \mathcal{K}_{p}\ {\color{red}{\varphi_{EC}}}}{{\color{red}{l^{2}_{EC}\ D_{EC}}}} + \frac{K_{EC}\ C_{out}^{2}\ {\color{red}{l^{2}_{EC}}}\ \mathcal{L}_{EC,d}\ \varphi_{EC}}{{\color{red}{l^{2}_{EC}}}\ D_{EC}}\Bigg)\\ \large C_{tot}\ =\ \Bigg\{K_{EC}\ C_{out}^{2} \mathcal{K}_{p}\ + \frac{K_{EC}\ C_{out}^{2}\ \mathcal{L}_{EC,d}\ \varphi_{EC}}{D_{EC}}\Bigg\} \frac{L_{cap}}{\mathcal{L}_{EC,d}}\\ \end{equation}
To account for the bound NC to other circulating cells (M),
\begin{equation} \normalsize C_{tot}\ =\ \Bigg\{K_{EC}\ \mathcal{K}_{p}\ C_{out}^{2}\ + \frac{K_{EC}\ \mathcal{L}_{EC,d}\ \varphi_{EC}}{D_{EC}} C_{out}^{2}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{EC,d}} + \Bigg\{\frac{K_{M}\ \mathcal{L}_{M,d}\ \varphi_{M}}{D_{M}} C_{out}^{2}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{M,d}} \end{equation}
To calculate the %idg:
\begin{equation} \normalsize \%idg \approx \frac{C_{tot}}{C_{out}}\ =\ \Bigg\{{\color{blue}{K_{EC}\ \mathcal{K}_{p}}}\ C_{out}\ + \frac{{\color{blue}{K_{EC}}}\ \mathcal{L}_{EC,d}\ \varphi_{EC}}{D_{EC}} C_{out}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{EC,d}} + \Bigg\{\frac{{\color{blue}{K_{M}}}\ \mathcal{L}_{M,d}\ \varphi_{M}}{D_{M}} C_{out}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{M,d}} \end{equation}
Here
- %idg = Percentage of injected dose per gram of tissue
- $C_{tot}$ = Total concentration of bound nanocarrier to endothelial cells and other capillary cells and nanocarriers within tissue
- $C_{out}$ = Concentration of nanocarrier in capillary
- $K_{EC}$ and $K_{M}$ = Association constant of nanocarrier to EC cells and other capillary cells respectively, where:
\begin{equation} K_a = \normalsize \Bigg( \frac{N_{ant}!}{(N_{ant} - n_b)! n_b!}\Bigg) \Bigg(\frac{N_{ab}}{n_b}\Bigg)\ \ \Bigg(\frac{\Delta\phi\Delta\theta\Delta\psi}{8\pi^2}\Bigg)\ \times \ \Bigg(\frac{\mathcal{A}_{R}^{b}}{\mathcal{A}_{R}^{u}}\Bigg)^{n_{b}}\ \ \frac{\mathcal{A}_{N}^{b}L_{z}}{(L_{z} - r^*)} \int^{r^*}_{0} dr\ exp(-\beta\mathcal{W}(r)). \end{equation}
- $N_{ant}$ = No. of receptors on membrane surface
- $n_b$ = No. of bonds formed upon binding between NC and tissue surface
- $N_{ab}$ = No. of antibodies per NC
- First two terms represent "Combinatorial binding for multivalent binding"
- Third term is called "NC rotational entropy"
- $\mathcal{A}^u_R$ = Average area traversed by an unbound receptor molecule = $\frac{\mathcal{A}}{N_{ant}}$
- $\mathcal{A}^b_R$ = Average area traversed by the bound receptor molecule.
- $L_z$ = Dimension of the simulation box along the reaction coordinate
- $r^*$ = cutoff length at which the conformational state of the NC and those of the membrane - receptor system cease to overlap (NC can only exist in unbound state)
- $exp(-\beta\mathcal{W}(r))$ = Boltzmann weight
- Forth term is called "translational entropy of receptors"
Fifth term is called "enthalpic contribution"
$\mathcal{K}_{p}$ = Partition coefficient
- $\varphi_EC$ and $\varphi_M$ = conc. of EC cells and Conc. of other capillary cells respectively in tissue.
- $D_{EC}$ and $D_M$ = diameter of EC cells and other capillary cells respectively.
- $\mathcal{L}_{EC,d}$ = length of the boundary layer within which the NC concentration is enhansed due to binding.
- $L_{cap}$ = size of the cell free layer in the capillary in which the NC is perfused.
All \%idg scores are presented in scaled units given by: \begin{equation} \Large \eta = \frac{(\%idg)_{org,sp}}{(\%idg)_{lung,sp}} \end{equation} where org = target organ and sp = species. \begin{equation} org = \text{target organ}\\ sp = \text{species}\\ \normalsize \eta_{sim} = \text{Computational model prediction}\\ \normalsize \eta_{exp} = \text{Experimental data} \end{equation}
The endothelial targeting of anti-ICAM1-coated NCs in five different organs in mouse are predited and compared to targeting levels measured in in vivo experiments.
In order to assess the predictive accuracy of the model, pearson's correlation coefficient are computed between model and experiment.
To make reliable predictions for an NC binding to the target tissue, it is essential to accuartely estimate the model parameter in the equation. This involves two sets of measurement:
- Estimation of ($\mathcal{k}$, $\mathcal{A}_{ex}$ and $N_{ant}$) to compute the $K_a$ Association coefficient for both EC and M cells
1) $\mathcal{k}$ = Effective bending rigidity bending modulus or bulk modulus. It is in the order of $k_BT$ and ranges from 10 - 100 $k_BT$. It is from the Canham - Helfrich phenomenological theory for membrances were
\begin{equation} \large \mathcal{H}_{elastic} = \int^L_0dx \int^L_0dy \Bigg\{\frac{\mathcal{k}}{2}(2H)^2+K_GG\Bigg\} \end{equation} where \begin{equation} \normalsize \mathcal{k} = \frac{Yh^3}{12(1-\sigma^2)}\\ Y = \text{Young's modulus}\\ \sigma = \text{Poisson's ration of the material}\\ h = \text{Thickness of membrance} \end{equation} 2) $\mathcal{A}_{ex}$ = Excess area of the membrane, From the model for the cell membrane:
\begin{equation} \large \mathcal{H}_{m} = \sum^{N_m}_{v=1} \frac{\mathcal{k}}{2} (c_{1,v}+c_{2,v})^2\mathcal{A}_v+\sigma\mathcal{A}_v \end{equation} where \begin{equation} \normalsize N_m = \text{Triangulated surface with $N_m$ nodes, $T_m$ triangles and $L_m$ links. such that $N_m +T_m -L_m = 0$}\\ \normalsize \sigma = \text{surface tension of the membrane}\\ \normalsize c_{1,v}\ and\ c_{2,v} = \text{two principle curvatures at vertex v}\\ \normalsize \mathcal{A}_v = \text{curvilinear area of te discrete membrane associated with vertex v, such that the total curvilinear area is:}\\ \normalsize \mathcal{A} = \sum^{N_m}_{v=1} \mathcal{A}_v\ \ \text{where}\ \ \mathcal{A}_{ex} = 100\frac{\mathcal{A}-\mathcal{A}_p}{\mathcal{A}}\\ \normalsize \text{which in terms of A:}\ \mathcal{A} = \frac{\mathcal{A}_p}{100-\mathcal{A}_{ex}} = \frac{\mathcal{L}^2}{100-\mathcal{A}_{ex}} \end{equation} Therefore, \begin{equation} \large \mathcal{H}_{m} = \sum^{N_m}_{v=1}\mathcal{A}_v \Bigg(\frac{\mathcal{k}}{2} (c_{1,v}+c_{2,v})^2+\sigma\Bigg)\\ \large \mathcal{H}_{m} = \frac{\mathcal{L}^2}{100-\mathcal{A}_{ex}} \Bigg(\frac{\mathcal{k}}{2} (c_{1,v}+c_{2,v})^2+\sigma\Bigg) \end{equation} with respect to the system (membrane), the actual values of $\mathcal{A}_{ex}$ is determined by the system of constant variables ( Temp, $\mathcal{A}_p$, $\sigma$)
3) including Nant, the Total energy of the NC - membrane system is given by
\begin{equation} \large \mathcal{H}_{t} = \mathcal{H}_{m} + \sum^{N_{ant}}_{i=1}\mathcal{H}_{f}(\theta_i) + \sum^{N_{ant}}_{i=1}\sum^{N_{ab}}_{j=1} \mathcal{H}_{b}(d_{ij}) \end{equation}
- Estimate for $\mathcal{K}_p$, $\varphi_X$, $\mathcal{L}_{X,b}$ and $D_x$ to compute the biodistribution.
In [3]:
from PIL import Image
basewidth = 1000
img = Image.open('nexpnsim.png')
hsize = int((float(img.size[1]) * float(basewidth / float(img.size[0]))))
img = img.resize((basewidth, hsize), Image.ANTIALIAS)
img
Out[3]:
\begin{equation} \normalsize \%idg \approx \frac{C_{tot}}{C_{out}}\ =\ \Bigg\{{\color{blue}{\mathcal{K}_{p}}} + \frac{K_{EC}\ \mathcal{L}_{EC,d}\ {\color{blue}{\varphi_{EC}}}}{D_{EC}} {\color{blue}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{EC,d}} + \Bigg\{\frac{K_{M}\ \mathcal{L}_{M,d}\ {\color{blue}{\varphi_{M}}}}{D_{M}} {\color{blue}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{M,d}} + \Bigg\{\frac{K_{EC}\ \mathcal{L}_{EC,d}\ {\color{blue}{\varphi_{Liver}}}}{D_{EC}} {\color{blue}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{EC,d}} \end{equation}
Loading libraries:¶
In [4]:
import numpy,os,sys,scipy
import scipy.stats.mstats as scp
import pickle
from mpl_toolkits.axes_grid.inset_locator import *
from scipy.stats.stats import pearsonr
from scipy import stats as ss
from bokeh.io import output_notebook, show
from bokeh.layouts import gridplot, row, column
from bokeh.plotting import figure
from bokeh.core.properties import value
from bokeh.models import ColumnDataSource, Whisker, Label
from bokeh.transform import dodge
from pprint import pprint
import pandas as pd
import numpy as np
from bokeh.io import show, output_notebook, push_notebook
from bokeh.plotting import figure
from bokeh.models import CategoricalColorMapper, HoverTool, ColumnDataSource, Panel
from bokeh.models.widgets import CheckboxGroup, Slider, RangeSlider, Tabs
from bokeh.layouts import column, row, WidgetBox
from bokeh.palettes import Category20_16
from bokeh.application.handlers import FunctionHandler
from bokeh.application import Application
from bokeh.io import export_png
output_notebook()
Function for creating the experimental dataset:¶
In [5]:
class compute_proteinlevels():
def __init__(self):
"class to compute the protein levels from existing data"
#data from Murciano et. al. 2003
self.m_murciano={'lung':16.8,'liver':4.71,'heart':1.07,'kidney':2.04,'spleen':5.38}
self.mp_murciano={}
#GCRMA scores from BIOGPS.org
self.m_biogps={'lung':8588.7,'liver':97.57,'heart':155.19,'kidney':133.09,'spleen':1395.32}
self.mp_biogps={}
#ppm scores from MOPED
self.m_moped={'lung':738,'liver':150,'heart':'none','kidney':'none','spleen':'none'}
self.mp_moped={}
#ppm scores from PAXDB
self.m_paxdb={'lung':407,'liver':29.8,'heart':0.07,'kidney':7.16,'spleen':26.8}
self.mp_paxdb={}
self.m_lung_ref=2000
#set the refernce value at 2000 antigens per square micron
self.mouseprotein={}
self.dbnames=['murciano','biogps','moped','paxdb','mean']
self.dbmouse={'murciano':self.m_murciano,'biogps':self.m_biogps,'moped':self.m_moped,'paxdb':self.m_paxdb}
self.dbmprot={'murciano':self.mp_murciano,'biogps':self.mp_biogps,'moped':self.mp_moped,'paxdb':self.mp_paxdb,'mean':self.mouseprotein}
#--------------------------------------------------------------------------------------------------------------------------------------------
def get_mouse_protein_exp(self,organs):
for organ in organs:
prot=[]
for dbname in self.dbnames[0:-1]:
db,pdb=self.dbmouse[dbname],self.dbmprot[dbname]
try:
protexp=self.m_lung_ref*db[organ.lower()]/db['lung']
pdb[organ.lower()]=protexp
prot.append(protexp)
except:
pdb[organ.lower()]='none'
self.mouseprotein[organ.lower()]=scp.gmean(prot)
return self.mouseprotein
Functions to create the simulation from model:¶
In [6]:
class compute_biodistribution:
"class to handle all calculation for computing the Ka values"
def __init__(self,model,kamode):
self.model = model
self.kamode = kamode
self.simtoexp_protconv = 4 # conversion between protein number in simulation to that in micron^2
self.antigendensity=[200,500,1000,2000,4000]
self.kappa = {'lung':40,'liver':40,'heart':40,'kidney':40,'spleen':40}
self.aex = {'lung':'medium','liver':'medium','heart':'small','kidney':'medium','spleen':'large'}
self.macrophage = {'lung':0.0,'liver':1.0,'heart':0.0,'kidney':0.0,'spleen':1.0} #data taken from J Exp Med. 1985, 161(3), 475--489
self.bldict = {'large':0.25,'medium':0.5,'small':1.5} #marker for excess area
#biodistribution as a function of antibody density for different tissues
self.antibody_dict ={41:'low',100:'medium',162:'high'}
#experimental scores for ICAM coated nanoparticles (experiments from Vlad)
self.IDg_Vlad_ICAM = {'lung':{'low':[23,7],'medium':[91,10],'high':[125,25]},
'liver':{'low':[85,10],'medium':[60,5],'high':[72,5]},
'spleen':{'low':[115,20],'medium':[60,15],'high':[75,25]},
'heart':{'low':[1,1],'medium':[2,1],'high':[2,1]},
'kidney':{'low':['none','none'],'medium':['none','none'],'high':['none','none']}}
#experimental scores for IgG coated nanoparticles (experiments from Vlad)
self.IDg_Vlad_IgG = {'lung':[15,2],'liver':[65,5],'spleen':[60,5],'heart':[1,1],'kidney':['none','none']}
# K_a values for various simulation systems
with open('./Computation/data/ka_relerror_dict-'+self.kamode+'.pkl','rb') as filenamesKa:
self.ka = pickle.load(filenamesKa, encoding='bytes')
# EC and macrophage parameters
self.ecdim = [0.3,5E-6,100E-9] #(10.1002/adem.201080076) # concentration, lateral size of Endothelial cells, and PMF range
self.mphdim = [0.03,5E-6,100E-9] #(doi:10.1083/jcb.50.2.498) # of Macrophages
#(nm^3)/particle to (molar)^{-1}
self.nm3tomolar = 6.023E-1
#------------------------------------------------------------------------------------------------
# used in compute_ka_values
# used in compute_ka_value
def get_antigen_limits(self,expression):
"compute the appropriate antigen limits for the given protein expression"
"(ex) for expression = 1500, returns 1000 and 2000 since 1500 falls between them."
#select the value of antigens that flanks the given expression number
# In "try", it looks for the values of antigendensity of [200,500,1000,2000,4000]
# for lung where expression is 1999.99, the results is the 3rd and 4th element for the for loop.
# It take the first element as of this list as its "firstval" value.
# if there is a expression value above 4000 such as 4001, then it get redirected to "except"
# here it takes the highest values in the antigendensity list which is 4000, the 4th element.
try:
firstval= [i for i in range(len(self.antigendensity)) if (self.antigendensity[i]-expression)>=0][0]
except:
firstval = len(self.antigendensity)-1
# here if the mouse expression level is lower than 200 and the firstval value is the 1st element
# then it takes the i1 value as 0 and i2 value as 200
# if the firstval value is above 0 then the for example the resulting value for lung will be
# i1 = 1000, i2 = 2000
if firstval==0:
i1,i2=0,self.antigendensity[firstval]
else:
i1,i2=self.antigendensity[firstval-1],self.antigendensity[firstval]
return [int(i1),int(i2)]
#------------------------------------------------------------------------------------------------
# used in compute_ka_values
# used in compute_ka_value
def get_kavalue(self,config,abval,antigennum,kappa=0.0,aex=0.0):
"get the correct Ka value"
if antigennum==0:
ka=[1,0]
else:
if config == 'membrane':
ka=self.ka[abval][antigennum/self.simtoexp_protconv][kappa][aex]
if config == 'flat':
ka=self.ka[abval][antigennum/self.simtoexp_protconv][b'flat']
return ka
#-----------------------------------------------------------------------------------------------
# used in process_Ka_with_error
def get_allvals(self,ka):
if ka[1]>0:
kae = [max(0,ka[0]-ka[0]*ka[1]),ka[0],ka[0]*(1+ka[1])]
else:
kae = [ka[0]]
return kae
#-----------------------------------------------------------------------------------------------
# used in compute_Ka_value
# used in compute_Ka_values
def process_idg_with_error(self,ecval,mphageval,iteration):
' receives a set of input parameters and computes %idg with error bar'
KIgG,ka1,ka2,dne1,dne2 = ecval
kp_mphage,mka1,mka2,dnm1,dnm2,MPConc = mphageval
Variable, value = iteration
if Variable == 'Cout':
Cout = value
elif Variable != 'Cout':
Cout = 1E-13
if Variable == 'Qec':
QEC = value/100
elif Variable != 'Qec':
QEC = self.ecdim[0]
if Variable == 'Qm':
QM = value/100
elif Variable != 'Qm':
QM = self.mphdim[0]
if Variable == 'Kp':
KP = value*KIgG
elif Variable != 'Kp':
KP = 1000*KIgG
pterm1 = QEC*self.ecdim[2]/self.ecdim[1]
pterm2 = MPConc*QM*self.mphdim[2]/self.mphdim[1]
kaec1,kaec2 = self.get_allvals(ka1),self.get_allvals(ka2)
kamp1,kamp2 = self.get_allvals(mka1),self.get_allvals(mka2)
idgval = []
for k1 in kaec1:
for k2 in kaec2:
KEC = (k1**dne1) * (k2**dne2)*self.nm3tomolar # interpolated value of KEC
if self.model == 'model1':
term1 = KIgG + KEC*pterm1
elif self.model == 'model2':
term1 = KP + KEC*pterm1*Cout
term2 = 0.0
for k3 in kamp1:
for k4 in kamp2:
KMP = (k3**dnm1) * (k4**dnm2) * self.nm3tomolar
if self.model == 'model1':
term2 = kp_mphage + KMP*pterm2
elif self.model == 'model2':
term2 = KMP*pterm2*Cout
kptarg = term1 + term2
if kptarg > 0.0:
kptarg = max(0,numpy.log10(kptarg))
else:
kptarg = 0.0
idgval.append(kptarg)
return idgval
def process_idg_with_errormode1(self,ecval,mphageval,mode5,organ,iteration):
' receives a set of input parameters and computes %idg with error bar'
KIgG,ka1,ka2,dne1,dne2 = ecval
kp_mphage,mka1,mka2,dnm1,dnm2,MPConc = mphageval
ka1mode1,ka2mode1,dne1mode1,dne2mode1 = mode5
Variable, value = iteration
if Variable == 'Cout':
Cout = value
elif Variable != 'Cout':
Cout = 1E-13
if Variable == 'Qec':
QEC = value/100
elif Variable != 'Qec':
QEC = self.ecdim[0]
if Variable == 'Qm':
QM = value/100
elif Variable != 'Qm':
QM = self.mphdim[0]
if Variable == 'Kp':
KP = value*KIgG
elif Variable != 'Kp':
KP = 1000*KIgG
if Variable == 'Qs':
QS = value/100
elif Variable != 'Qs':
QS = self.ecdim[0]
pterm1 = QEC*self.ecdim[2]/self.ecdim[1]
pterm2 = MPConc*QM*self.mphdim[2]/self.mphdim[1]
if organ == 'liver':
pterm3 = QS*self.ecdim[2]/self.ecdim[1]
else: pterm3 = 0
kaec1,kaec2 = self.get_allvals(ka1),self.get_allvals(ka2)
kamp1,kamp2 = self.get_allvals(mka1),self.get_allvals(mka2)
kaec1mode1, kaec2mode1 = self.get_allvals(ka1mode1),self.get_allvals(ka2mode1)
idgval = []
if organ == 'liver':
for k1 in kaec1:
for k2 in kaec2:
KEC = (k1**dne1) * (k2**dne2)*self.nm3tomolar # interpolated value of KEC
if self.model == 'model1':
term1 = KIgG + KEC*pterm1
elif self.model == 'model2':
term1 = KP + KEC*pterm1*Cout
term2 = 0.0
for k3 in kamp1:
for k4 in kamp2:
KMP = (k3**dnm1) * (k4**dnm2) * self.nm3tomolar
if self.model == 'model1':
term2 = kp_mphage + KMP*pterm2
elif self.model == 'model2':
term2 = KP*pterm2*Cout
term3 = 0.0
for k5 in kaec1mode1:
for k6 in kaec2mode1:
KS = (k5**dne1mode1) * (k6**dne2mode1) * self.nm3tomolar
term3 = KS*pterm3*Cout
kptarg = term1 + term2 + term3
if kptarg > 0.0:
kptarg = max(0,numpy.log10(kptarg))
else:
kptarg = 0.0
idgval.append(kptarg)
else:
for k1 in kaec1:
for k2 in kaec2:
KEC = (k1**dne1) * (k2**dne2)*self.nm3tomolar # interpolated value of KEC
if self.model == 'model1':
term1 = KIgG + KEC*pterm1
elif self.model == 'model2':
term1 = KP + KEC*pterm1*Cout
term2 = 0.0
for k3 in kamp1:
for k4 in kamp2:
KMP = (k3**dnm1) * (k4**dnm2) * self.nm3tomolar
if self.model == 'model1':
term2 = kp_mphage + KMP*pterm2
elif self.model == 'model2':
term2 = KMP*pterm2*Cout
kptarg = term1 + term2
if kptarg > 0.0:
kptarg = max(0,numpy.log10(kptarg))
else:
kptarg = 0.0
idgval.append(kptarg)
return idgval
#------------------------------------------------------------------------------------------------
# used in main code
def compute_idg_values(self,organs,protexpr,abval,KpIgG,mphage,concentration,components,index,debug = False):
"Compute the %idg value for a given organ"
if index[2] != 'mode5':
kavalues={}
MPConc, KIgG, KEC, KMP = 0.0,0.0,0.0,0.0
df = pd.DataFrame([])
for count, organ in enumerate(organs):
# The lower() command turns the organs list elements to lower cases.
# then the protexpr takes the specific exp. protein expression data and saves it as protnum.
# for example the first organs in the list is Lung and its Protein expression level is 1999.99
# exprlim is the i1 and i2 values that enclose the protnum value from the antigendensity list
# antigendensity list = 200, 500, 1000, 2000, 4000.
# For example in lung, the values enclosing its protnum = 1999.99 is 1000 and 2000
protnum = protexpr[organ.lower()] # protein level for organ (expt)
exprlim = self.get_antigen_limits(protnum) # the protein expressions for interpolation (sim)
# Here kappa is 40 for all organs
# aex is medium for lung, liver and kidney, small for heart and large for spleen
# bldict is large = 0.25, medium = 0.5 and small = 1.5
# for example, for the command ka=biodistrib.ka[41][250][40][0.5], the value of ka = [957444515750678.2, 2.6112391596329734]
kptarg = 0.0
if components[0] == 'membrane':
kappa = self.kappa[organ.lower()] # kappa for organ (expt)
aex = self.bldict[self.aex[organ.lower()]] # aex for organ (expt)
ka1 = self.get_kavalue('membrane',abval,exprlim[0],kappa,aex) # Ka value for the lower limit (sim)
ka2 = self.get_kavalue('membrane',abval,exprlim[1],kappa,aex) # Ka value for the upper limit (sim)
elif components[0] == 'flat':
ka1 = self.get_kavalue('flat',abval,exprlim[0]) # Ka value for the lower limit (flat substrate sim)
ka2 = self.get_kavalue('flat',abval,exprlim[1]) # Ka value for the upper limit (flat substrate sim)
else:
print ('invalid option for computing Ka values \n')
print ('you supplied : ', components)
print ('valid options for components[0]="flat" or "membrane"')
print ('exiting')
sys.exit()
# this takes the KpIgG_mouse values, eg) for lung[15, 2], this take the KIgG = 15 for lung.
if components[2] == 'KpIgG':
if KpIgG[organ][0] != 'none':
KIgG = KpIgG[organ][0]
dne1,dne2 = 1.0*(exprlim[1]-protnum)/(exprlim[1]-exprlim[0]),1.0*(protnum-exprlim[0])/(exprlim[1]-exprlim[0]) # interpolation limit for protein expr.
term2,pterm2 = 0.0, 0.0
mka1,mka2 = [0,0],[0,0]
dnm1,dnm2 = 0,0
kp_mphage = 0.0
if components[1] == 'mphage':
protnum1 = mphage[1]
exprlim1 = self.get_antigen_limits(protnum1) # the protein expressions for interpolation (sim)
mka1 = self.get_kavalue('membrane',mphage[0],exprlim1[0],mphage[2],mphage[3]) # Ka value for the lower limit (sim)
mka2 = self.get_kavalue('membrane',mphage[0],exprlim1[1],mphage[2],mphage[3]) # Ka value for the upper limit (sim)
dnm1,dnm2 = 1.0*(exprlim1[1]-protnum1)/(exprlim1[1]-exprlim1[0]),1.0*(protnum1-exprlim1[0])/(exprlim1[1]-exprlim1[0])
kp_mphage = KIgG
MPConc = self.macrophage[organ.lower()] # Macrophage Conc (sim)
idgval = self.process_idg_with_error([KIgG,ka1,ka2,dne1,dne2],[kp_mphage,mka1,mka2,dnm1,dnm2,MPConc],[index[0],index[1]]) #combine values to form an idg with errorbar
kpfinal,kperror = numpy.mean(idgval),numpy.sqrt(numpy.std(idgval))
IND = pd.MultiIndex.from_product([[index[0]],[index[1]],[index[2]],[organ]],
names = ['Local Variable','Value','Mode','Organ'])
columns = pd.MultiIndex.from_product([['idg','idgSD','AllidgValues'], [abval]],
names=['Results', 'Abval'])
data = [[kpfinal, kperror, idgval]]
matrix = pd.DataFrame(data, index=IND, columns=columns)
if count == 0:
allmatrix = matrix
else:
allmatrix = allmatrix.append(matrix)
elif index[2] == 'mode5':
kavalues={}
MPConc, KIgG, KEC, KMP = 0.0,0.0,0.0,0.0
df = pd.DataFrame([])
for count, organ in enumerate(organs):
# The lower() command turns the organs list elements to lower cases.
# then the protexpr takes the specific exp. protein expression data and saves it as protnum.
# for example the first organs in the list is Lung and its Protein expression level is 1999.99
# exprlim is the i1 and i2 values that enclose the protnum value from the antigendensity list
# antigendensity list = 200, 500, 1000, 2000, 4000.
# For example in lung, the values enclosing its protnum = 1999.99 is 1000 and 2000
protnum = protexpr[organ.lower()] # protein level for organ (expt)
exprlim = self.get_antigen_limits(protnum) # the protein expressions for interpolation (sim)
# Here kappa is 40 for all organs
# aex is medium for lung, liver and kidney, small for heart and large for spleen
# bldict is large = 0.25, medium = 0.5 and small = 1.5
# for example, for the command ka=biodistrib.ka[41][250][40][0.5], the value of ka = [957444515750678.2, 2.6112391596329734]
kptarg = 0.0
if components[0] == 'membrane':
kappa = self.kappa[organ.lower()] # kappa for organ (expt)
aex = self.bldict[self.aex[organ.lower()]] # aex for organ (expt)
ka1 = self.get_kavalue('membrane',abval,exprlim[0],kappa,aex) # Ka value for the lower limit (sim)
ka2 = self.get_kavalue('membrane',abval,exprlim[1],kappa,aex) # Ka value for the upper limit (sim)
else:
print ('invalid option for computing Ka values \n')
print ('you supplied : ', components)
print ('valid options for components[0]="flat" or "membrane"')
print ('exiting')
sys.exit()
# calucation for mode 1 part of mode 5:
ka1mode1 = self.get_kavalue('flat',abval,exprlim[0]) # Ka value for the lower limit (flat substrate sim)
ka2mode1 = self.get_kavalue('flat',abval,exprlim[1]) # Ka value for the upper limit (flat substrate sim)
# this takes the KpIgG_mouse values, eg) for lung[15, 2], this take the KIgG = 15 for lung.
if components[2] == 'KpIgG':
if KpIgG[organ][0] != 'none':
KIgG = KpIgG[organ][0]
dne1,dne2 = 1.0*(exprlim[1]-protnum)/(exprlim[1]-exprlim[0]),1.0*(protnum-exprlim[0])/(exprlim[1]-exprlim[0]) # interpolation limit for protein expr.
term2,pterm2 = 0.0, 0.0
mka1,mka2 = [0,0],[0,0]
dnm1,dnm2 = 0,0
kp_mphage = 0.0
dne1mode1,dne2mode1 = dne1,dne2
if components[1] == 'mphage':
protnum1 = mphage[1]
exprlim1 = self.get_antigen_limits(protnum1) # the protein expressions for interpolation (sim)
mka1 = self.get_kavalue('membrane',mphage[0],exprlim1[0],mphage[2],mphage[3]) # Ka value for the lower limit (sim)
mka2 = self.get_kavalue('membrane',mphage[0],exprlim1[1],mphage[2],mphage[3]) # Ka value for the upper limit (sim)
dnm1,dnm2 = 1.0*(exprlim1[1]-protnum1)/(exprlim1[1]-exprlim1[0]),1.0*(protnum1-exprlim1[0])/(exprlim1[1]-exprlim1[0])
kp_mphage = KIgG
MPConc = self.macrophage[organ.lower()] # Macrophage Conc (sim)
idgval = self.process_idg_with_errormode1([KIgG,ka1,ka2,dne1,dne2],[kp_mphage,mka1,mka2,dnm1,dnm2,MPConc],[ka1mode1,ka2mode1,dne1mode1,dne2mode1],organ,[index[0],index[1]]) #combine values to form an idg with errorbar
kpfinal,kperror = numpy.mean(idgval),numpy.sqrt(numpy.std(idgval))
IND = pd.MultiIndex.from_product([[index[0]],[index[1]],[index[2]],[organ]],
names = ['Local Variable','Value','Mode','Organ'])
columns = pd.MultiIndex.from_product([['idg','idgSD','AllidgValues'], [abval]],
names=['Results', 'Abval'])
data = [[kpfinal, kperror, idgval]]
matrix = pd.DataFrame(data, index=IND, columns=columns)
if count == 0:
allmatrix = matrix
else:
allmatrix = allmatrix.append(matrix)
if debug:
print (organ,'->',abval,'->',KIgG,'->',KEC,'->',KMP,'->',kpfinal,'->',kperror)
print ('\n------------------------------------------------->\n')
return allmatrix
#------------------------------------------------------------------------------------------------
def NormalizeIdgValue(self,organs,abdens,simdata,ExpData = False):
n,n1=0,0
maxval,maxval1=[],[]
d={}
for fo in abdens:
d["{0}count".format(fo)]=[]
d["{0}error".format(fo)]=[]
d["{0}upper".format(fo)]=[]
d["{0}lower".format(fo)]=[]
for organ in organs:
m=0
for ab in abdens:
if ExpData == False:
sdata = simdata[ab][organ.lower()]
elif ExpData == True:
sdata = self.IDg_Vlad_ICAM[organ.lower()][self.antibody_dict[ab]]
if sdata[0] != 'none':
d["{0}count".format(ab)].append(sdata[0])
d["{0}error".format(ab)].append(sdata[1])
d["{0}upper".format(ab)].append(sdata[0]+sdata[1])
d["{0}lower".format(ab)].append(sdata[0]-sdata[1])
if sdata[0] == 'none':
d["{0}count".format(ab)].append(0)
d["{0}error".format(ab)].append(0)
d["{0}upper".format(ab)].append(0)
d["{0}lower".format(ab)].append(0)
n1 += 1
m += 1
n += 1
if ExpData == False:
normval = simdata[162]['lung'][0]
elif ExpData == True:
normval=self.IDg_Vlad_ICAM['lung']['high'][0]
div=[]
for div in abdens:
d["{0}count".format(div)]= list(map(lambda x: x/normval, d["{0}count".format(div)]))
d["{0}error".format(div)]= list(map(lambda x: x/normval, d["{0}error".format(div)]))
d["{0}upper".format(div)]= list(map(lambda x: x/normval, d["{0}upper".format(div)]))
d["{0}lower".format(div)]= list(map(lambda x: x/normval, d["{0}lower".format(div)]))
d["organs"] = organs
if ExpData == True:
datadict={}
for ab in abdens:
datadict1={}
for organ in organs:
exdata = self.IDg_Vlad_ICAM[organ.lower()][self.antibody_dict[ab]]
if exdata[0] != 'none':
datadict1[organ]=exdata
datadict[ab]=datadict1
elif ExpData == False:
datadict = []
return d, datadict
#-----------------------------------------------------------------------------------------------------------------------------------------------------
def vlad_experiments(self,organs,abdens):
for count1, ab in enumerate(abdens):
for count2, organ in enumerate(organs):
exdata = self.IDg_Vlad_ICAM[organ.lower()][self.antibody_dict[ab]]
IND = pd.MultiIndex.from_product([['Exp'],['Exp'],['Exp'],[organ]], names = ['Local Variable','Value','Mode','Organ'])
columns = pd.MultiIndex.from_product([['idg','idgSD','AllidgValues'], [ab]], names=['Results', 'Abval'])
data = [[exdata[0], exdata[1],0]]
matrix = pd.DataFrame(data, index=IND, columns=columns)
if count2 == 0: allmatrix = matrix
else: allmatrix = allmatrix.append(matrix)
if count1 == 0: Result = allmatrix
else: Result = pd.concat([Result, allmatrix], axis = 1)
return Result
#-----------------------------------------------------------------------------------------------------------------------------------------------------
# used in compute_correlation_expt
def draw_normaldistribution(mean,errbar,nsamples):
data = sorted(numpy.random.normal(mean,errbar,nsamples))
return data
#-----------------------------------------------------------------------------------------------------------------------------------------------------
# used in main code
# def compute_correlation_expt(d1,d2,ab,organ):
# nsamples = 10000 #set this variable to a large number
# nlen = len(organ)
# if nlen == 1:
# a,b = numpy.zeros(len(ab)*nlen*nsamples),numpy.zeros(len(ab)*nlen*nsamples)
# null_hyp,obs_val = numpy.zeros(len(ab)*nlen), numpy.zeros(len(ab)*nlen)
# elif nlen >= 2 and nlen <=5:
# a,b = numpy.zeros(len(ab)*(nlen-1)*nsamples),numpy.zeros(len(ab)*(nlen-1)*nsamples)
# null_hyp,obs_val = numpy.zeros(len(ab)*(nlen-1)), numpy.zeros(len(ab)*(nlen-1))
# normval1, normval2 = 1.0*d1[162]['lung'][0],1.0*d2[162]['lung'][0]
# npartition = 5
# partsize = int(nsamples/npartition) # 10000/5 = 2000
# pc = 0
# rsq = []
# for npart in range(npartition): # for range in 5
# pcs = pc
# for nab in ab: # 41, 100, 162
# for norgan in organ: # Organ
# try:
# m1,m2 = d1[nab][norgan][0],d2[nab][norgan][0]
# s1,s2 = d1[nab][norgan][1],d2[nab][norgan][1]
# a[pc:pc+partsize] = draw_normaldistribution(m1/normval1,s1/normval1,partsize)
# b[pc:pc+partsize] = draw_normaldistribution(m2/normval2,s2/normval2,partsize)
# pc += partsize
# except:
# pass
# rsq.append(pearsonr(a[pcs:pc],b[pcs:pc])[0])
# return [(pearsonr(a,b)[0]),rsq]
#-----------------------------------------------------------------------------------------------------------------------------------------------------
def make_plot(source, modetitle, LLegend = False):
p = figure(x_range=organs, title = modetitle, y_range = (0, 1.5), plot_height = 250, plot_width = 500, y_axis_label = '\u03b7 sim')
color = ["#98df8a","#718dbf","#e84d60"]
p.vbar(x=dodge('organs', -0.25, range=p.x_range), top='41count', width=0.2, source=source,color=color[0], legend=value("41"))
p.vbar(x=dodge('organs', 0.0, range=p.x_range), top='100count', width=0.2, source=source,color=color[1], legend=value("100"))
p.vbar(x=dodge('organs', 0.25, range=p.x_range), top='162count', width=0.2, source=source,color=color[2], legend=value("162"))
p.add_layout(Whisker(source=source, base=dodge('organs', -0.25, range=p.x_range), upper="41upper", lower="41lower", level="overlay"))
p.add_layout(Whisker(source=source, base=dodge('organs', 0.0, range=p.x_range), upper="100upper", lower="100lower", level="overlay"))
p.add_layout(Whisker(source=source, base=dodge('organs', 0.25, range=p.x_range), upper="162upper", lower="162lower", level="overlay"))
p.x_range.range_padding = 0.1
p.xgrid.grid_line_color = None
p.legend.visible = False
if LLegend == True:
p.yaxis.axis_label = '\u03b7 exp'
p.legend.visible = True
p.legend.location = "top_right"
p.legend.orientation = "horizontal"
return p
def compute_correlation_expt(d1,d2,ab,organ):
nsamples = 10000 #set this variable to a large number
nlen = len(organ)
if nlen == 1:
a,b = numpy.zeros(len(ab)*nlen*nsamples),numpy.zeros(len(ab)*nlen*nsamples)
elif nlen >= 2 and nlen <=5:
a,b = numpy.zeros(len(ab)*(nlen-1)*nsamples),numpy.zeros(len(ab)*(nlen-1)*nsamples)
normval1, normval2 = 1.0*d1[('idg',162)]['lung'] ,1.0*d2[('idg',162)]['lung']
npartition = 5
partsize = int(nsamples/npartition)
pc = 0
for npart in range(npartition):
for nab in ab:
for norgan in organ:
try:
m1,m2 = d1[('idg',nab)][norgan],d2[('idg',nab)][norgan]
s1,s2 = d1[('idgSD',nab)][norgan],d2[('idgSD',nab)][norgan]
a[pc:pc+partsize] = draw_normaldistribution(m1/normval1,s1/normval1,partsize)
b[pc:pc+partsize] = draw_normaldistribution(m2/normval2,s2/normval2,partsize)
pc += partsize
except:
pass
return [pearsonr(a,b)[0]]
Main code to generate the DataMatrix:¶
In [7]:
#____________________________________________________________________________________
# Calculating Mouse Experimental Data:
#organs=['lung','heart','kidney','liver','spleen']
organs=['lung','liver']
protlev = compute_proteinlevels() # class for protein level expression
mouselevels = protlev.get_mouse_protein_exp(organs)
#____________________________________________________________________________________
# Calculating Simulation Data for Mode 1 to Mode 5:
# Note: These values deterimine which simulation model and Kamode the Ka values should be taken from within the 'data' folder to calculate the %idg values.
model = 'model2'
kamode = 'm0'
mp_k,mp_bl,mp_antI,mp_antA=160,0.25,100,250
abdens=[41,100,162]
ncconcentration=1.0
mode = ['mode1','mode2','mode3','mode4','mode5']
mode1 = ['flat','none','KpIgG']
mode2 = ['membrane','none','KpIgG']
mode3 = ['membrane','mphage','KpIgG']
mode4 = ['membrane','mphage','KpIgG']
mode5 = ['membrane','mphage','KpIgG']
ModeTitle = ["(i) Experimental Data",
"(ii) Flat Substrate Simulation",
"(iii) Membrane Simulation",
"(iv) Membrane + Inactive Macrophage Simulation",
"(v) Membrane + Active Membrane Simulation"
"(iv) Membrane + Active Membrane + High Liver Simulation"]
Variable = ['Cout','Qec', 'Qm', 'Kp', 'Qs']
COUT = [1E-9, 1E-10, 1E-11, 1E-12, 1E-13]
QEC = [3, 6, 9, 12, 15, 18, 21, 24, 27, 30]
QM = [3, 6, 9, 12, 15, 18, 21, 24, 27, 30]
KP = [1, 10, 100, 1000]
QS = [3, 6, 9, 12, 15, 18, 21, 24, 27, 30]
# _____________________________________________
# Calculating idg values for each organ with respect to number of antigen for each mode
# Compute Biodistribution in mouse for model and kamode:
biodistrib = compute_biodistribution(model, kamode) # class for experimental and simulation results
KpIgG_mouse = biodistrib.IDg_Vlad_IgG # IDg scores for IgG coated particles as measure of untargeted partition coefficient
for vart, Var0 in enumerate(Variable):
if Var0 == 'Cout': VarVal = COUT
elif Var0 == 'Qec': VarVal = QEC
elif Var0 == 'Qm': VarVal = QM
elif Var0 == 'Kp': VarVal = KP
elif Var0 == 'Qs': VarVal = QS
for valt, Value in enumerate(VarVal):
for t, ab in enumerate(abdens):
print ("Running --- idg --- ",Var0," --- ", Value, " --- ", ab)
matrix = pd.DataFrame()
inactive_muphage = [ab,mp_antI,160,0.25]
#----
matrix1 = biodistrib.compute_idg_values(organs,mouselevels,ab,KpIgG_mouse,inactive_muphage,ncconcentration,mode1,[Var0, Value,'mode1'])
matrix = matrix1
matrix2 = biodistrib.compute_idg_values(organs,mouselevels,ab,KpIgG_mouse,inactive_muphage,ncconcentration,mode2,[Var0, Value,'mode2'])
matrix = matrix1.append(matrix2)
matrix3 = biodistrib.compute_idg_values(organs,mouselevels,ab,KpIgG_mouse,inactive_muphage,ncconcentration,mode3,[Var0, Value,'mode3'])
matrix = matrix.append(matrix3)
active_muphage = [ab,mp_antA,40,1.5]
matrix4 = biodistrib.compute_idg_values(organs,mouselevels,ab,KpIgG_mouse,active_muphage,ncconcentration,mode4,[Var0, Value,'mode4'])
matrix = matrix.append(matrix4)
matrix5 = biodistrib.compute_idg_values(organs,mouselevels,ab,KpIgG_mouse,active_muphage,ncconcentration,mode5,[Var0, Value,'mode5'])
matrix = matrix.append(matrix5)
if t == 0:
allmatrix = matrix
else:
allmatrix = pd.concat([allmatrix, matrix], axis = 1)
if valt == 0:
allmatrixval = allmatrix
else:
allmatrixval = pd.concat([allmatrixval, allmatrix])
if vart == 0:
allmatrixvar = allmatrixval
else:
allmatrixvar = pd.concat([allmatrixval, allmatrixvar])
# Normalizing the idg values:
for abd in abdens:
allmatrixvar['normIDG',abd] = 0.0
allmatrixvar['normIDGsd',abd] = 0.0
for Var0 in Variable:
if Var0 == 'Cout': VarVal = COUT
elif Var0 == 'Qec': VarVal = QEC
elif Var0 == 'Qm': VarVal = QM
elif Var0 == 'Kp': VarVal = KP
elif Var0 == 'Qs': VarVal = QS
for Value in VarVal:
for mod in mode:
for organ in organs:
for ad in abdens:
print ("Running --- normIDG --- ",Var0," --- ", Value, " --- ", mod, " --- ", organ, " --- ", ab)
allmatrixvar.loc[(Var0, Value, mod, organ),('normIDG',ad)] = allmatrixvar.loc[(Var0, Value, mod, organ),('idg',ad)]/(allmatrixvar.loc[(Var0, Value, mod, 'lung'),('idg',162)])
allmatrixvar.loc[(Var0, Value, mod, organ),('normIDGsd',ad)] = allmatrixvar.loc[(Var0, Value, mod, organ),('idgSD',ad)]/(allmatrixvar.loc[(Var0, Value, mod, 'lung'),('idg',162)])
#den = allmatrixvar.loc[(Var0, Value, mod, 'lung'),('idg',162)]
#idglist = allmatrixvar.loc[(Var0, Value, mod, organ),('AllidgValues',ad)]
#newlist = [ x/den for x in idglist]
#allmatrixvar.loc[(Var0, Value, mod, organ),('normIDGall',ad)] = newlist
# Concatinating Experimental data:
expdata = biodistrib.vlad_experiments(organs,abdens)
Var0,Value,mod = 'Exp','Exp','Exp'
for organ in organs:
for ad in abdens:
print ("Running --- Exp --- ",organ," --- ", ad)
expdata.loc[(Var0, Value, mod, organ),('normIDG',ad)] = expdata.loc[(Var0, Value, mod, organ),('idg',ad)]/(expdata.loc[(Var0, Value, mod, 'lung'),('idg',162)])
expdata.loc[(Var0, Value, mod, organ),('normIDGsd',ad)] = expdata.loc[(Var0, Value, mod, organ),('idgSD',ad)]/(expdata.loc[(Var0, Value, mod, 'lung'),('idg',162)])
DataMatrix = pd.concat([allmatrixvar, expdata])
# R square calculation:
DataMatrix['Rsqrt'] = 0.0
DataMatrix['RsqrtALL'] = 0.0
Exp = DataMatrix.loc[('Exp','Exp','Exp'),(['idg','idgSD'])]
organlist=[['lung'],['liver']]
for Var0 in Variable:
if Var0 == 'Cout': VarVal = COUT
elif Var0 == 'Qec': VarVal = QEC
elif Var0 == 'Qm': VarVal = QM
elif Var0 == 'Kp': VarVal = KP
elif Var0 == 'Qs': VarVal = QS
for Value in VarVal:
for mod in mode:
for organ in organlist:
print ("Running --- RSquare --- ",Var0," --- ", Value, " --- ", mod, " --- ", organ)
Sim = DataMatrix.loc[(Var0,Value,mod),(['idg','idgSD'])]
Rsquare = compute_correlation_expt(Exp, Sim, abdens, organ)
DataMatrix.loc[(Var0,Value,mod,organ),(['Rsqrt'])] = Rsquare
Sim = DataMatrix.loc[(Var0,Value,mod),(['idg','idgSD'])]
Rsquare = compute_correlation_expt(Exp, Sim, abdens, organs)
for org in organs:
for i in Rsquare:
DataMatrix.loc[(Var0,Value,mod,org),(['RsqrtALL'])] = i
# CF calculation:
DataMatrix['CF'] = 0.0
DataMatrix['CFALL'] = 0.0
for Var0 in Variable:
if Var0 == 'Cout': VarVal = COUT
elif Var0 == 'Qec': VarVal = QEC
elif Var0 == 'Qm': VarVal = QM
elif Var0 == 'Kp': VarVal = KP
elif Var0 == 'Qs': VarVal = QS
for Value in VarVal:
for mod in mode:
for organ in organs:
Experiment = DataMatrix.loc[('Exp','Exp','Exp',organ),('normIDG')]
Simulat = DataMatrix.loc[(Var0,Value,mod,organ),('normIDG')]
denom = (Experiment.mean()**2)*len(Experiment)
numer = []
for abd in abdens:
numer.append((Experiment[abd]-Simulat[abd])**2)
result = ((sum(numer))/denom)
DataMatrix.loc[(Var0,Value,mod,organ),('CF')] = result
# CFALL caluclation:
Experiment = DataMatrix.loc[('Exp','Exp','Exp'),('normIDG')]
Experiment = Experiment.unstack()
denom = (Experiment.mean()**2)*len(Experiment)
for Var0 in Variable:
if Var0 == 'Cout': VarVal = COUT
elif Var0 == 'Qec': VarVal = QEC
elif Var0 == 'Qm': VarVal = QM
elif Var0 == 'Kp': VarVal = KP
elif Var0 == 'Qs': VarVal = QS
for Value in VarVal:
for mod in mode:
Simulat = DataMatrix.loc[(Var0,Value,mod),('normIDG')]
Simulat = Simulat.unstack()
numer = []
for abd in abdens:
for organ in organs:
numer.append((Experiment[abd,organ]-Simulat[abd,organ])**2)
result = ((sum(numer))/denom)
DataMatrix.loc[(Var0,Value,mod),('CFALL')] = result
DataMatrix¶
In [8]:
DataMatrix
Out[8]:
In [9]:
DataMatrix.to_csv("Part1DataMatrix", sep='\t')
In [10]:
from pandas import ExcelWriter
writer = ExcelWriter('Kp1000LungLiver.xlsx')
DataMatrix.to_excel(writer)
writer.save()
DataMatrix Statistics:¶
In [11]:
DataMatrix.describe()[(['idg','normIDG','Rsqrt','RsqrtALL','CF','CFALL'])]
Out[11]:
DataMatrix Experimental dataset:¶
In [12]:
DataMatrix.loc[('Exp','Exp','Exp'), (['idg','normIDG','Rsqrt','RsqrtALL','CF','CFALL'])]
Out[12]:
DataMatrix with the default values: (from the Cout = 1E-13 set)¶
In [13]:
DataMatrix.loc[('Cout',1E-13),(['idg','normIDG','Rsqrt','RsqrtALL','CF','CFALL'])]
# expdataidglung = DataMatrix.loc[('Exp','Exp','Exp','lung'), (['normIDG'])].unstack()[41].tolist()[0]
Out[13]:
Plotting bar graphs¶
In [14]:
DataMatrix.loc[('Exp','Exp','Exp'), (['normIDG'])]
Out[14]:
In [15]:
DataMatrix.loc[('Cout',1E-13),(['normIDG'])]
Out[15]:
In [16]:
def make_plot(source, modetitle, LLegend = False, Values = True):
sourcessss = ColumnDataSource(source)
p = figure(x_range=organs,
title = modetitle,
y_range = (0, 2),
plot_height = 300,
plot_width = 500,
y_axis_label = '\u03b7 sim')
color = ["#98df8a","#718dbf","#e84d60"]
p.vbar(x=dodge('organs', -0.25, range=p.x_range), top='41count', width=0.2, source=sourcessss, color=color[0], legend=value("41"))
p.vbar(x=dodge('organs', 0.0, range=p.x_range), top='100count', width=0.2, source=sourcessss, color=color[1], legend=value("100"))
p.vbar(x=dodge('organs', 0.25, range=p.x_range), top='162count', width=0.2, source=sourcessss, color=color[2], legend=value("162"))
p.add_layout(Whisker(source=sourcessss, base=dodge('organs', -0.25, range=p.x_range), upper="41upper", lower="41lower", level="overlay"))
p.add_layout(Whisker(source=sourcessss, base=dodge('organs', 0.0, range=p.x_range), upper="100upper", lower="100lower", level="overlay"))
p.add_layout(Whisker(source=sourcessss, base=dodge('organs', 0.25, range=p.x_range), upper="162upper", lower="162lower", level="overlay"))
p.x_range.range_padding = 0.1
p.xgrid.grid_line_color = None
p.legend.visible = False
if LLegend == True:
p.yaxis.axis_label = '\u03b7 exp'
p.legend.visible = True
p.legend.location = "top_right"
p.legend.orientation = "horizontal"
if Values == True:
mytext = Label(x=0.2, y=1.8, text=' r\u00B2 = '+str(source['R2lung'][0]), background_fill_color = "grey", background_fill_alpha = 0.1, border_line_color = "black", border_line_alpha = 0.5)
p.add_layout(mytext)
mytext = Label(x=0.8, y=1.8, text=' r\u00B2 All= '+str(source['R2All'][0]), background_fill_color = "wheat", background_fill_alpha = 0.1, border_line_color = "black", border_line_alpha = 0.5)
p.add_layout(mytext)
mytext = Label(x=3, y=1.8, text=' r\u00B2 = '+str(source['R2liver'][0]), background_fill_color = "wheat", background_fill_alpha = 0.1, border_line_color = "black", border_line_alpha = 0.5)
p.add_layout(mytext)
mytext = Label(x=0.2, y=1.5, text=' CF = '+str(source['CFlung'][0]), background_fill_color = "grey", background_fill_alpha = 0.1, border_line_color = "black", border_line_alpha = 0.5)
p.add_layout(mytext)
mytext = Label(x=0.8, y=1.5, text=' CF All= '+str(source['CFAll'][0]), background_fill_color = "wheat", background_fill_alpha = 0.1, border_line_color = "black", border_line_alpha = 0.5)
p.add_layout(mytext)
mytext = Label(x=1.5, y=1.5, text=' CF = '+str(source['CFliver'][0]), background_fill_color = "wheat", background_fill_alpha = 0.1, border_line_color = "black", border_line_alpha = 0.5)
p.add_layout(mytext)
return p
In [17]:
kvalue ={}
Experimentalplot = {}
expdataidglung = DataMatrix.loc[('Exp','Exp','Exp','lung'), (['normIDG'])].unstack()[41].tolist()[0]
expdataidgsdlung = DataMatrix.loc[('Exp','Exp','Exp','lung'), (['normIDGsd'])].unstack()[41].tolist()[0]
expdataidgliver = DataMatrix.loc[('Exp','Exp','Exp','liver'), (['normIDG'])].unstack()[41].tolist()[0]
expdataidgsdliver = DataMatrix.loc[('Exp','Exp','Exp','liver'), (['normIDGsd'])].unstack()[41].tolist()[0]
Experimentalplot['41count']= [expdataidglung, expdataidgliver]
Experimentalplot['41upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
Experimentalplot['41lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
expdataidglung = DataMatrix.loc[('Exp','Exp','Exp','lung'), (['normIDG'])].unstack()[100].tolist()[0]
expdataidgsdlung = DataMatrix.loc[('Exp','Exp','Exp','lung'), (['normIDGsd'])].unstack()[100].tolist()[0]
expdataidgliver = DataMatrix.loc[('Exp','Exp','Exp','liver'), (['normIDG'])].unstack()[100].tolist()[0]
expdataidgsdliver = DataMatrix.loc[('Exp','Exp','Exp','liver'), (['normIDGsd'])].unstack()[100].tolist()[0]
Experimentalplot['100count']= [expdataidglung, expdataidgliver]
Experimentalplot['100upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
Experimentalplot['100lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
expdataidglung = DataMatrix.loc[('Exp','Exp','Exp','lung'), (['normIDG'])].unstack()[162].tolist()[0]
expdataidgsdlung = DataMatrix.loc[('Exp','Exp','Exp','lung'), (['normIDGsd'])].unstack()[162].tolist()[0]
expdataidgliver = DataMatrix.loc[('Exp','Exp','Exp','liver'), (['normIDG'])].unstack()[162].tolist()[0]
expdataidgsdliver = DataMatrix.loc[('Exp','Exp','Exp','liver'), (['normIDGsd'])].unstack()[162].tolist()[0]
Experimentalplot['162count']= [expdataidglung, expdataidgliver]
Experimentalplot['162upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
Experimentalplot['162lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
Experimentalplot['organs']=['lung', 'liver']
In [18]:
# # _________________________________________
plotvariable ='Qs'
plotValue=30
plotmode='mode1'
mode1idg = {}
expdataidglung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDG'])].unstack()[41].tolist()[0]
expdataidgsdlung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDGsd'])].unstack()[41].tolist()[0]
expdataidgliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDG'])].unstack()[41].tolist()[0]
expdataidgsdliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDGsd'])].unstack()[41].tolist()[0]
mode1idg['41count']= [expdataidglung, expdataidgliver]
mode1idg['41upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
mode1idg['41lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
expdataidglung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDG'])].unstack()[100].tolist()[0]
expdataidgsdlung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDGsd'])].unstack()[100].tolist()[0]
expdataidgliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDG'])].unstack()[100].tolist()[0]
expdataidgsdliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDGsd'])].unstack()[100].tolist()[0]
mode1idg['100count']= [expdataidglung, expdataidgliver]
mode1idg['100upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
mode1idg['100lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
expdataidglung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDG'])].unstack()[162].tolist()[0]
expdataidgsdlung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDGsd'])].unstack()[162].tolist()[0]
expdataidgliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDG'])].unstack()[162].tolist()[0]
expdataidgsdliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDGsd'])].unstack()[162].tolist()[0]
mode1idg['162count']= [expdataidglung, expdataidgliver]
mode1idg['162upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
mode1idg['162lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
mode1idg['R2lung'] = [round(DataMatrix.loc[('Cout',1E-13,'mode1','lung'),(['Rsqrt'])].tolist()[0],3), 0]
mode1idg['R2liver'] = [round(DataMatrix.loc[('Cout',1E-13,'mode1','liver'),(['Rsqrt'])].tolist()[0],3), 0]
mode1idg['R2All'] = [round(DataMatrix.loc[('Cout',1E-13,'mode1','lung'),(['RsqrtALL'])].tolist()[0],3), 0]
mode1idg['CFlung'] = [round(DataMatrix.loc[('Cout',1E-13,'mode1','lung'),(['CF'])].tolist()[0],3), 0]
mode1idg['CFliver'] = [round(DataMatrix.loc[('Cout',1E-13,'mode1','liver'),(['CF'])].tolist()[0],3), 0]
mode1idg['CFAll'] = [round(DataMatrix.loc[('Cout',1E-13,'mode1','lung'),(['CFALL'])].tolist()[0],3), 0]
mode1idg['organs']=['lung', 'liver']
# # _________________________________________
plotmode='mode2'
mode2idg = {}
expdataidglung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDG'])].unstack()[41].tolist()[0]
expdataidgsdlung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDGsd'])].unstack()[41].tolist()[0]
expdataidgliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDG'])].unstack()[41].tolist()[0]
expdataidgsdliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDGsd'])].unstack()[41].tolist()[0]
mode2idg['41count']= [expdataidglung, expdataidgliver]
mode2idg['41upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
mode2idg['41lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
expdataidglung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDG'])].unstack()[100].tolist()[0]
expdataidgsdlung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDGsd'])].unstack()[100].tolist()[0]
expdataidgliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDG'])].unstack()[100].tolist()[0]
expdataidgsdliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDGsd'])].unstack()[100].tolist()[0]
mode2idg['100count']= [expdataidglung, expdataidgliver]
mode2idg['100upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
mode2idg['100lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
expdataidglung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDG'])].unstack()[162].tolist()[0]
expdataidgsdlung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDGsd'])].unstack()[162].tolist()[0]
expdataidgliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDG'])].unstack()[162].tolist()[0]
expdataidgsdliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDGsd'])].unstack()[162].tolist()[0]
mode2idg['162count']= [expdataidglung, expdataidgliver]
mode2idg['162upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
mode2idg['162lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
mode2idg['R2lung'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'),(['Rsqrt'])].tolist()[0],3), 0]
mode2idg['R2liver'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'),(['Rsqrt'])].tolist()[0],3), 0]
mode2idg['R2All'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'),(['RsqrtALL'])].tolist()[0],3), 0]
mode2idg['CFlung'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'),(['CF'])].tolist()[0],3), 0]
mode2idg['CFliver'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'),(['CF'])].tolist()[0],3), 0]
mode2idg['CFAll'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'),(['CFALL'])].tolist()[0],3), 0]
mode2idg['organs']=['lung', 'liver']
# # _________________________________________
plotmode='mode3'
mode3idg = {}
expdataidglung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDG'])].unstack()[41].tolist()[0]
expdataidgsdlung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDGsd'])].unstack()[41].tolist()[0]
expdataidgliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDG'])].unstack()[41].tolist()[0]
expdataidgsdliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDGsd'])].unstack()[41].tolist()[0]
mode3idg['41count']= [expdataidglung, expdataidgliver]
mode3idg['41upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
mode3idg['41lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
expdataidglung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDG'])].unstack()[100].tolist()[0]
expdataidgsdlung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDGsd'])].unstack()[100].tolist()[0]
expdataidgliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDG'])].unstack()[100].tolist()[0]
expdataidgsdliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDGsd'])].unstack()[100].tolist()[0]
mode3idg['100count']= [expdataidglung, expdataidgliver]
mode3idg['100upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
mode3idg['100lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
expdataidglung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDG'])].unstack()[162].tolist()[0]
expdataidgsdlung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDGsd'])].unstack()[162].tolist()[0]
expdataidgliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDG'])].unstack()[162].tolist()[0]
expdataidgsdliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDGsd'])].unstack()[162].tolist()[0]
mode3idg['162count']= [expdataidglung, expdataidgliver]
mode3idg['162upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
mode3idg['162lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
mode3idg['R2lung'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'),(['Rsqrt'])].tolist()[0],3), 0]
mode3idg['R2liver'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'),(['Rsqrt'])].tolist()[0],3), 0]
mode3idg['R2All'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'),(['RsqrtALL'])].tolist()[0],3), 0]
mode3idg['CFlung'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'),(['CF'])].tolist()[0],3), 0]
mode3idg['CFliver'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'),(['CF'])].tolist()[0],3), 0]
mode3idg['CFAll'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'),(['CFALL'])].tolist()[0],3), 0]
mode3idg['organs']=['lung', 'liver']
# # _________________________________________
plotmode='mode4'
mode4idg = {}
expdataidglung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDG'])].unstack()[41].tolist()[0]
expdataidgsdlung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDGsd'])].unstack()[41].tolist()[0]
expdataidgliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDG'])].unstack()[41].tolist()[0]
expdataidgsdliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDGsd'])].unstack()[41].tolist()[0]
mode4idg['41count']= [expdataidglung, expdataidgliver]
mode4idg['41upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
mode4idg['41lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
expdataidglung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDG'])].unstack()[100].tolist()[0]
expdataidgsdlung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDGsd'])].unstack()[100].tolist()[0]
expdataidgliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDG'])].unstack()[100].tolist()[0]
expdataidgsdliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDGsd'])].unstack()[100].tolist()[0]
mode4idg['100count']= [expdataidglung, expdataidgliver]
mode4idg['100upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
mode4idg['100lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
expdataidglung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDG'])].unstack()[162].tolist()[0]
expdataidgsdlung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDGsd'])].unstack()[162].tolist()[0]
expdataidgliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDG'])].unstack()[162].tolist()[0]
expdataidgsdliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDGsd'])].unstack()[162].tolist()[0]
mode4idg['162count']= [expdataidglung, expdataidgliver]
mode4idg['162upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
mode4idg['162lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
mode4idg['R2lung'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'),(['Rsqrt'])].tolist()[0],3), 0]
mode4idg['R2liver'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'),(['Rsqrt'])].tolist()[0],3), 0]
mode4idg['R2All'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'),(['RsqrtALL'])].tolist()[0],3), 0]
mode4idg['CFlung'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'),(['CF'])].tolist()[0],3), 0]
mode4idg['CFliver'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'),(['CF'])].tolist()[0],3), 0]
mode4idg['CFAll'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'),(['CFALL'])].tolist()[0],3), 0]
mode4idg['organs']=['lung', 'liver']
# # _________________________________________
plotmode='mode5'
mode5idg = {}
expdataidglung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDG'])].unstack()[41].tolist()[0]
expdataidgsdlung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDGsd'])].unstack()[41].tolist()[0]
expdataidgliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDG'])].unstack()[41].tolist()[0]
expdataidgsdliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDGsd'])].unstack()[41].tolist()[0]
mode5idg['41count']= [expdataidglung, expdataidgliver]
mode5idg['41upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
mode5idg['41lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
expdataidglung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDG'])].unstack()[100].tolist()[0]
expdataidgsdlung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDGsd'])].unstack()[100].tolist()[0]
expdataidgliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDG'])].unstack()[100].tolist()[0]
expdataidgsdliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDGsd'])].unstack()[100].tolist()[0]
mode5idg['100count']= [expdataidglung, expdataidgliver]
mode5idg['100upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
mode5idg['100lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
expdataidglung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDG'])].unstack()[162].tolist()[0]
expdataidgsdlung = DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'), (['normIDGsd'])].unstack()[162].tolist()[0]
expdataidgliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDG'])].unstack()[162].tolist()[0]
expdataidgsdliver = DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'), (['normIDGsd'])].unstack()[162].tolist()[0]
mode5idg['162count']= [expdataidglung, expdataidgliver]
mode5idg['162upper']= [expdataidglung+expdataidgsdlung, expdataidgliver+expdataidgsdliver]
mode5idg['162lower']= [expdataidglung-expdataidgsdlung, expdataidgliver-expdataidgsdliver]
mode5idg['R2lung'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'),(['Rsqrt'])].tolist()[0],3), 0]
mode5idg['R2liver'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'),(['Rsqrt'])].tolist()[0],3), 0]
mode5idg['R2All'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'),(['RsqrtALL'])].tolist()[0],3), 0]
mode5idg['CFlung'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'),(['CF'])].tolist()[0],3), 0]
mode5idg['CFliver'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'liver'),(['CF'])].tolist()[0],3), 0]
mode5idg['CFAll'] = [round(DataMatrix.loc[(plotvariable,plotValue,plotmode,'lung'),(['CFALL'])].tolist()[0],3), 0]
mode5idg['organs']=['lung', 'liver']
In [19]:
ModeTitle = ["(i) Experimental Data", "(ii) Flat Substrate Simulation", "(iii) Membrane Simulation", "(iv) Membrane + Inactive Macrophage Simulation", "(v) Membrane + Active Macrophage Simulation", "(vi) Membrane + Active Macrophage + High Liver Macrophage"]
p = make_plot(Experimentalplot, ModeTitle[0], LLegend = True, Values = False)
p1 = make_plot(mode1idg, ModeTitle[1], LLegend = False, Values = True)
p2 = make_plot(mode2idg, ModeTitle[2], LLegend = False, Values = True)
p3 = make_plot(mode3idg, ModeTitle[3], LLegend = False, Values = True)
p4 = make_plot(mode4idg, ModeTitle[4], LLegend = False, Values = True)
p5 = make_plot(mode5idg, ModeTitle[5], LLegend = False, Values = True)
show(column(p,p1, p2, p3, p4, p5))
pall = gridplot([[p],
[p1],
[p2],
[p3],
[p4],
[p5]])
show(pall)
from bokeh.io import export_png
export_png(pall, filename="plots/kp1000/Qs30.png")
Out[19]:
Cout Plots - R2, CF, R2 vs CF:¶
\begin{equation} \normalsize \%idg \approx \frac{C_{tot}}{C_{out}}\ =\ \Bigg\{{\color{blue}{\mathcal{K}_{p}}} + \frac{K_{EC}\ \mathcal{L}_{EC,d}\ {\color{blue}{\varphi_{EC}}}}{D_{EC}} {\color{red}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{EC,d}} + \Bigg\{\frac{K_{M}\ \mathcal{L}_{M,d}\ {\color{blue}{\varphi_{M}}}}{D_{M}} {\color{red}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{M,d}} + \Bigg\{\frac{K_{EC}\ \mathcal{L}_{EC,d}\ {\color{blue}{\varphi_{S}}}}{D_{EC}} {\color{red}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{EC,d}} \end{equation}
Cout DataMatrix Statistics:¶
In [20]:
DataMatrix.loc[('Cout')].describe()[(['idg','normIDG','Rsqrt','RsqrtALL','CF','CFALL'])]
Out[20]:
Cout vs R2 Single plot:¶
In [21]:
# Plotting Cout vs R2 all in one plot
p = figure(title="Cout vs R2 ",x_axis_type="log", plot_width=900)
COUT = [1E-13, 1E-12, 1E-11, 1E-10, 1E-9]
#------------
Linemode1 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = p.line(COUT, Linemode1all['lung'], line_width=3, line_color="tomato", muted_color="tomato", muted_alpha=0.1)
r12 = p.line(COUT, Linemode1['liver'], line_width=3, line_color="purple", muted_color="purple", muted_alpha=0.1)
r13 = p.line(COUT, Linemode1['lung'], line_width=3, line_color="blue", muted_color="blue", muted_alpha=0.1)
# r14 = p.line(COUT, Linemode1['heart'], line_width=3, line_color="green", muted_color="green", muted_alpha=0.1)
#------------
Linemode2 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = p.line(COUT, Linemode2all['lung'], line_width=3, line_color="tomato", line_dash="dashed", muted_color="tomato", muted_alpha=0.1)
r22 = p.line(COUT, Linemode2['liver'], line_width=3, line_color="purple", line_dash="dashed", muted_color="purple", muted_alpha=0.1)
r23 = p.line(COUT, Linemode2['lung'], line_width=3, line_color="blue", line_dash="dashed", muted_color="blue", muted_alpha=0.1)
# r24 = p.line(COUT, Linemode2['heart'], line_width=3, line_color="green", line_dash="dashed", muted_color="green", muted_alpha=0.1)
#------------
Linemode3 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = p.line(COUT, Linemode3all['lung'], line_width=3, line_color="tomato", line_dash="dotted", muted_color="tomato", muted_alpha=0.1)
r32 = p.line(COUT, Linemode3['liver'], line_width=3, line_color="purple", line_dash="dotted", muted_color="purple", muted_alpha=0.1)
r33 = p.line(COUT, Linemode3['lung'], line_width=3, line_color="blue", line_dash="dotted", muted_color="blue", muted_alpha=0.1)
# r34 = p.line(COUT, Linemode3['heart'], line_width=3, line_color="green", line_dash="dotted", muted_color="green", muted_alpha=0.1)
#------------
Linemode4 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = p.line(COUT, Linemode4all['lung'], line_width=3, line_color="tomato", line_dash="dotdash", muted_color="tomato", muted_alpha=0.1)
r42 = p.line(COUT, Linemode4['liver'], line_width=3, line_color="purple", line_dash="dotdash", muted_color="purple", muted_alpha=0.1)
r43 = p.line(COUT, Linemode4['lung'], line_width=3, line_color="blue", line_dash="dotdash", muted_color="blue", muted_alpha=0.1)
# r44 = p.line(COUT, Linemode4['heart'], line_width=3, line_color="green", line_dash="dotdash", muted_color="green", muted_alpha=0.1)
#------------
Linemode5 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = p.line(COUT, Linemode5all['lung'], line_width=3, line_color="tomato", line_dash="dashdot", muted_color="tomato", muted_alpha=0.1)
r52 = p.line(COUT, Linemode5['liver'], line_width=3, line_color="purple", line_dash="dashdot", muted_color="purple", muted_alpha=0.1)
r53 = p.line(COUT, Linemode5['lung'], line_width=3, line_color="blue", line_dash="dashdot", muted_color="blue", muted_alpha=0.1)
# r54 = p.line(COUT, Linemode5['heart'], line_width=3, line_color="green", line_dash="dashdot", muted_color="green", muted_alpha=0.1)
#------------
from bokeh.models import Legend
legend = Legend(items = [
("Mode1 - All Organs", [r11]),
("Mode1 - Liver", [r12]),
("Mode1 - Lung", [r13]),
#("Mode1 - Heart", [r14]),
("Mode2 - All Organs", [r21]),
("Mode2 - Liver", [r22]),
("Mode2 - Lung", [r23]),
#("Mode2 - Heart", [r24]),
("Mode3 - All Organs", [r31]),
("Mode3 - Liver", [r32]),
("Mode3 - Lung", [r33]),
#("Mode3 - Heart", [r34]),
("Mode4 - All Organs", [r41]),
("Mode4 - Liver", [r42]),
("Mode4 - Lung", [r43]),
#("Mode4 - Heart", [r44]),
("Mode5 - All Organs", [r51]),
("Mode5 - Liver", [r52]),
("Mode5 - Lung", [r53])], location = (0, -30))
#("Mode5 - Heart", [r54])], location = (0, -30))
p.add_layout(legend, "right")
p.legend.click_policy="mute"
p.xaxis.axis_label = 'Domain'
p.yaxis.axis_label = 'Values'
show(p)
export_png(p, filename="plots/kp1000/R2Coutsingle.png")
Out[21]:
Cout vs R2 Multi plot:¶
In [22]:
# Plotting Cout vs R2 in seperate plots:
COUT = [1E-13, 1E-12, 1E-11, 1E-10, 1E-9]
#------------
Linemode1 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - All")
r11.line(COUT, Linemode1all['lung'], line_width=3, line_color="tomato")
r12 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Liver")
r12.line(COUT, Linemode1['liver'], line_width=3, line_color="purple")
# r12.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r13 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Lung")
r13.line(COUT, Linemode1['lung'], line_width=3, line_color="blue")
# r14 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Heart")
# r14.line(COUT, Linemode1['heart'], line_width=3, line_color="green")
# #------------
Linemode2 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - All")
r21.line(COUT, Linemode2all['lung'], line_width=3, line_color="tomato")
r22 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Liver")
r22.line(COUT, Linemode2['liver'], line_width=3, line_color="purple")
# r22.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r23 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Lung")
r23.line(COUT, Linemode2['lung'], line_width=3, line_color="blue")
# r24 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Heart")
# r24.line(COUT, Linemode2['heart'], line_width=3, line_color="green")
# #------------
Linemode3 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - All")
r31.line(COUT, Linemode3all['lung'], line_width=3, line_color="tomato")
r32 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Liver")
r32.line(COUT, Linemode3['liver'], line_width=3, line_color="purple")
# r32.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r33 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Lung")
r33.line(COUT, Linemode3['lung'], line_width=3, line_color="blue")
# r34 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Heart")
# r34.line(COUT, Linemode3['heart'], line_width=3, line_color="green")
# #------------
Linemode4 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - All")
r41.line(COUT, Linemode4all['lung'], line_width=3, line_color="tomato")
r42 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Liver")
r42.line(COUT, Linemode4['liver'], line_width=3, line_color="purple")
# r42.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r43 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Lung")
r43.line(COUT, Linemode4['lung'], line_width=3, line_color="blue")
# r44 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Heart")
# r44.line(COUT, Linemode4['heart'], line_width=3, line_color="green")
# #------------
Linemode5 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - All")
r51.line(COUT, Linemode5all['lung'], line_width=3, line_color="tomato")
r52 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Liver")
r52.line(COUT, Linemode5['liver'], line_width=3, line_color="purple")
# r52.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r53 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Lung")
r53.line(COUT, Linemode5['lung'], line_width=3, line_color="blue")
# r54 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Heart")
# r54.line(COUT, Linemode5['heart'], line_width=3, line_color="green")
# #------------
p = gridplot([[r11, r21, r31, r41, r51],
[r12, r22, r32, r42, r52],
[r13, r23, r33, r43, r53]])
show(p)
export_png(p, filename="plots/kp1000/R2Coutmulti.png")
Out[22]:
Cout vs CF Single plot:¶
In [23]:
# Plotting Cout vs R2 all in one plot
p = figure(title="Cout vs CF",x_axis_type="log", plot_width=900, y_range= (0,1))
COUT = [1E-13, 1E-12, 1E-11, 1E-10, 1E-9]
#------------
Linemode1 = DataMatrix.loc[('Cout'),('CF')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Cout'),('CFALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = p.line(COUT, Linemode1all['lung'], line_width=3, line_color="tomato", muted_color="tomato", muted_alpha=0.1)
r12 = p.line(COUT, Linemode1['liver'], line_width=3, line_color="purple", muted_color="purple", muted_alpha=0.1)
r13 = p.line(COUT, Linemode1['lung'], line_width=3, line_color="blue", muted_color="blue", muted_alpha=0.1)
# r14 = p.line(COUT, Linemode1['heart'], line_width=3, line_color="green", muted_color="green", muted_alpha=0.1)
#------------
Linemode2 = DataMatrix.loc[('Cout'),('CF')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Cout'),('CFALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = p.line(COUT, Linemode2all['lung'], line_width=3, line_color="tomato", line_dash="dashed", muted_color="tomato", muted_alpha=0.1)
r22 = p.line(COUT, Linemode2['liver'], line_width=3, line_color="purple", line_dash="dashed", muted_color="purple", muted_alpha=0.1)
r23 = p.line(COUT, Linemode2['lung'], line_width=3, line_color="blue", line_dash="dashed", muted_color="blue", muted_alpha=0.1)
# r24 = p.line(COUT, Linemode2['heart'], line_width=3, line_color="green", line_dash="dashed", muted_color="green", muted_alpha=0.1)
#------------
Linemode3 = DataMatrix.loc[('Cout'),('CF')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Cout'),('CFALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = p.line(COUT, Linemode3all['lung'], line_width=3, line_color="tomato", line_dash="dotted", muted_color="tomato", muted_alpha=0.1)
r32 = p.line(COUT, Linemode3['liver'], line_width=3, line_color="purple", line_dash="dotted", muted_color="purple", muted_alpha=0.1)
r33 = p.line(COUT, Linemode3['lung'], line_width=3, line_color="blue", line_dash="dotted", muted_color="blue", muted_alpha=0.1)
# r34 = p.line(COUT, Linemode3['heart'], line_width=3, line_color="green", line_dash="dotted", muted_color="green", muted_alpha=0.1)
#------------
Linemode4 = DataMatrix.loc[('Cout'),('CF')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Cout'),('CFALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = p.line(COUT, Linemode4all['lung'], line_width=3, line_color="tomato", line_dash="dotdash", muted_color="tomato", muted_alpha=0.1)
r42 = p.line(COUT, Linemode4['liver'], line_width=3, line_color="purple", line_dash="dotdash", muted_color="purple", muted_alpha=0.1)
r43 = p.line(COUT, Linemode4['lung'], line_width=3, line_color="blue", line_dash="dotdash", muted_color="blue", muted_alpha=0.1)
# r44 = p.line(COUT, Linemode4['heart'], line_width=3, line_color="green", line_dash="dotdash", muted_color="green", muted_alpha=0.1)
#------------
Linemode5 = DataMatrix.loc[('Cout'),('CF')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Cout'),('CFALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = p.line(COUT, Linemode5all['lung'], line_width=3, line_color="tomato", line_dash="dashdot", muted_color="tomato", muted_alpha=0.1)
r52 = p.line(COUT, Linemode5['liver'], line_width=3, line_color="purple", line_dash="dashdot", muted_color="purple", muted_alpha=0.1)
r53 = p.line(COUT, Linemode5['lung'], line_width=3, line_color="blue", line_dash="dashdot", muted_color="blue", muted_alpha=0.1)
# r54 = p.line(COUT, Linemode5['heart'], line_width=3, line_color="green", line_dash="dashdot", muted_color="green", muted_alpha=0.1)
#------------
# from bokeh.models import Legend
legend = Legend(items = [
("Mode1 - All Organs", [r11]),
("Mode1 - Liver", [r12]),
("Mode1 - Lung", [r13]),
# ("Mode1 - Heart", [r14]),
("Mode2 - All Organs", [r21]),
("Mode2 - Liver", [r22]),
("Mode2 - Lung", [r23]),
# ("Mode2 - Heart", [r24]),
("Mode3 - All Organs", [r31]),
("Mode3 - Liver", [r32]),
("Mode3 - Lung", [r33]),
# ("Mode3 - Heart", [r34]),
("Mode4 - All Organs", [r41]),
("Mode4 - Liver", [r42]),
("Mode4 - Lung", [r43]),
# ("Mode4 - Heart", [r44]),
("Mode5 - All Organs", [r51]),
("Mode5 - Liver", [r52]),
("Mode5 - Lung", [r53])], location = (0, -30))
# ("Mode5 - Heart", [r54])
p.add_layout(legend, "right")
p.legend.click_policy="mute"
p.xaxis.axis_label = 'Domain'
p.yaxis.axis_label = 'Values'
show(p)
export_png(p, filename="plots/kp1000/CFCoutSingle.png")
Out[23]:
Cout vs CF Multi plot:¶
In [24]:
# Plotting Cout vs R2 in seperate plots:
COUT = [1E-13, 1E-12, 1E-11, 1E-10, 1E-9]
#------------
Linemode1 = DataMatrix.loc[('Cout'),('CF')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Cout'),('CFALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - All")
r11.line(COUT, Linemode1all['lung'], line_width=3, line_color="tomato")
r12 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Liver")
r12.line(COUT, Linemode1['liver'], line_width=3, line_color="purple")
# r12.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r13 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Lung")
r13.line(COUT, Linemode1['lung'], line_width=3, line_color="blue")
# r14 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode1 - Heart")
# r14.line(COUT, Linemode1['heart'], line_width=3, line_color="green")
# #------------
Linemode2 = DataMatrix.loc[('Cout'),('CF')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Cout'),('CFALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - All")
r21.line(COUT, Linemode2all['lung'], line_width=3, line_color="tomato")
r22 = figure(x_axis_type="log", y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode2 - Liver")
r22.line(COUT, Linemode2['liver'], line_width=3, line_color="purple")
# r22.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r23 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Lung")
r23.line(COUT, Linemode2['lung'], line_width=3, line_color="blue")
# r24 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode2 - Heart")
# r24.line(COUT, Linemode2['heart'], line_width=3, line_color="green")
# #------------
Linemode3 = DataMatrix.loc[('Cout'),('CF')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Cout'),('CFALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - All")
r31.line(COUT, Linemode3all['lung'], line_width=3, line_color="tomato")
r32 = figure(x_axis_type="log", y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode3 - Liver")
r32.line(COUT, Linemode3['liver'], line_width=3, line_color="purple")
# r32.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r33 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Lung")
r33.line(COUT, Linemode3['lung'], line_width=3, line_color="blue")
# r34 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode3 - Heart")
# r34.line(COUT, Linemode3['heart'], line_width=3, line_color="green")
# #------------
Linemode4 = DataMatrix.loc[('Cout'),('CF')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Cout'),('CFALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - All")
r41.line(COUT, Linemode4all['lung'], line_width=3, line_color="tomato")
r42 = figure(x_axis_type="log", y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode4 - Liver")
r42.line(COUT, Linemode4['liver'], line_width=3, line_color="purple")
# r42.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r43 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Lung")
r43.line(COUT, Linemode4['lung'], line_width=3, line_color="blue")
# r44 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode4 - Heart")
# r44.line(COUT, Linemode4['heart'], line_width=3, line_color="green")
# #------------
Linemode5 = DataMatrix.loc[('Cout'),('CF')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Cout'),('CFALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - All")
r51.line(COUT, Linemode5all['lung'], line_width=3, line_color="tomato")
r52 = figure(x_axis_type="log", y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode5 - Liver")
r52.line(COUT, Linemode5['liver'], line_width=3, line_color="purple")
# r52.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r53 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Lung")
r53.line(COUT, Linemode5['lung'], line_width=3, line_color="blue")
# r54 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode5 - Heart")
# r54.line(COUT, Linemode5['heart'], line_width=3, line_color="green")
# #------------
p = gridplot([[r11, r21, r31, r41, r51],
[r12, r22, r32, r42, r52],
[r13, r23, r33, r43, r53]])
show(p)
from bokeh.io import export_png
export_png(p, filename="plots/kp1000/CFCoutMulti.png")
Out[24]:
R2 vs CF for Cout single plot:¶
In [25]:
# Plotting R2 vs CF for Cout all in one plot
p = figure(title="R2 vs CF ", plot_width=900, x_range = (0, 1), y_range = (0, 1)) #
COUT = [1E-13, 1E-12, 1E-11, 1E-10, 1E-9]
#------------
CFmode1 = DataMatrix.loc[('Cout'),('CF')].xs('mode1',level='Mode')
CFmode1 = CFmode1.unstack()
CFALLmode1 = DataMatrix.loc[('Cout'),('CFALL')].xs('mode1',level='Mode')
CFALLmode1 = CFALLmode1.unstack()
Rmode1 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode1',level='Mode')
Rmode1 = Rmode1.unstack()
RALLmode1 = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode1',level='Mode')
RALLmode1 = RALLmode1.unstack()
r11 = p.triangle( CFALLmode1['lung'] , RALLmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r12 = p.square( CFmode1['liver'] , Rmode1['liver'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r13 = p.circle( CFmode1['lung'] , Rmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
#------------
CFmode2 = DataMatrix.loc[('Cout'),('CF')].xs('mode2',level='Mode')
CFmode2 = CFmode2.unstack()
CFALLmode2 = DataMatrix.loc[('Cout'),('CFALL')].xs('mode2',level='Mode')
CFALLmode2 = CFALLmode2.unstack()
Rmode2 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode2',level='Mode')
Rmode2 = Rmode2.unstack()
RALLmode2 = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode2',level='Mode')
RALLmode2 = RALLmode2.unstack()
r21 = p.triangle( CFALLmode2['lung'] , RALLmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r22 = p.square( CFmode2['liver'] , Rmode2['liver'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r23 = p.circle( CFmode2['lung'] , Rmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
#------------
CFmode3 = DataMatrix.loc[('Cout'),('CF')].xs('mode3',level='Mode')
CFmode3 = CFmode3.unstack()
CFALLmode3 = DataMatrix.loc[('Cout'),('CFALL')].xs('mode3',level='Mode')
CFALLmode3 = CFALLmode3.unstack()
Rmode3 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode3',level='Mode')
Rmode3 = Rmode3.unstack()
RALLmode3 = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode3',level='Mode')
RALLmode3 = RALLmode3.unstack()
r31 = p.triangle( CFALLmode3['lung'] , RALLmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r32 = p.square( CFmode3['liver'] , Rmode3['liver'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r33 = p.circle( CFmode3['lung'] , Rmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
#------------
CFmode4 = DataMatrix.loc[('Cout'),('CF')].xs('mode4',level='Mode')
CFmode4 = CFmode4.unstack()
CFALLmode4 = DataMatrix.loc[('Cout'),('CFALL')].xs('mode4',level='Mode')
CFALLmode4 = CFALLmode4.unstack()
Rmode4 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode4',level='Mode')
Rmode4 = Rmode4.unstack()
RALLmode4 = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode4',level='Mode')
RALLmode4 = RALLmode4.unstack()
r41 = p.triangle( CFALLmode4['lung'] , RALLmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r42 = p.square( CFmode4['liver'] , Rmode4['liver'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r43 = p.circle( CFmode4['lung'] , Rmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
#------------
CFmode5 = DataMatrix.loc[('Cout'),('CF')].xs('mode5',level='Mode')
CFmode5 = CFmode5.unstack()
CFALLmode5 = DataMatrix.loc[('Cout'),('CFALL')].xs('mode5',level='Mode')
CFALLmode5 = CFALLmode5.unstack()
Rmode5 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode5',level='Mode')
Rmode5 = Rmode5.unstack()
RALLmode5 = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode5',level='Mode')
RALLmode5 = RALLmode5.unstack()
r51 = p.triangle( CFALLmode5['lung'] , RALLmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r52 = p.square( CFmode5['liver'] , Rmode5['liver'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r53 = p.circle( CFmode5['lung'] , Rmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
legend = Legend(items = [
("Mode1 - All Organs", [r11]),
("Mode1 - Liver", [r12]),
("Mode1 - Lung", [r13]),
("Mode2 - All Organs", [r21]),
("Mode2 - Liver", [r22]),
("Mode2 - Lung", [r23]),
("Mode3 - All Organs", [r31]),
("Mode3 - Liver", [r32]),
("Mode3 - Lung", [r33]),
("Mode4 - All Organs", [r41]),
("Mode4 - Liver", [r42]),
("Mode4 - Lung", [r43]),
("Mode5 - All Organs", [r51]),
("Mode5 - Liver", [r52]),
("Mode5 - Lung", [r53])], location = (0, -30))
p.add_layout(legend, "right")
p.legend.click_policy="mute"
p.xaxis.axis_label = 'CF'
p.yaxis.axis_label = 'R2'
show(p)
export_png(p, filename="plots/kp1000/CFR2CoutSingle.png")
Out[25]:
R2 vs CF for Cout Miltiple plot:¶
In [26]:
# Plotting Cout vs R2 in seperate plots:
#------------
p = figure(title="R2 vs CF ", plot_width=900, x_range = (0, 1), y_range = (0, 1)) #
COUT = [1E-13, 1E-12, 1E-11, 1E-10, 1E-9]
#------------
CFmode1 = DataMatrix.loc[('Cout'),('CF')].xs('mode1',level='Mode')
CFmode1 = CFmode1.unstack()
CFALLmode1 = DataMatrix.loc[('Cout'),('CFALL')].xs('mode1',level='Mode')
CFALLmode1 = CFALLmode1.unstack()
Rmode1 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode1',level='Mode')
Rmode1 = Rmode1.unstack()
RALLmode1 = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode1',level='Mode')
RALLmode1 = RALLmode1.unstack()
r11 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode1 - All")
r11.triangle( CFALLmode1['lung'] , RALLmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r12 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode1 - Liver")
r12.square( CFmode1['liver'] , Rmode1['liver'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r13 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode1 - Lung")
r13.circle( CFmode1['lung'] , Rmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
# ----------
CFmode2 = DataMatrix.loc[('Cout'),('CF')].xs('mode2',level='Mode')
CFmode2 = CFmode2.unstack()
CFALLmode2 = DataMatrix.loc[('Cout'),('CFALL')].xs('mode2',level='Mode')
CFALLmode2 = CFALLmode2.unstack()
Rmode2 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode2',level='Mode')
Rmode2 = Rmode2.unstack()
RALLmode2 = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode2',level='Mode')
RALLmode2 = RALLmode2.unstack()
r21 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode2 - All")
r21.triangle( CFALLmode2['lung'] , RALLmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r22 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode2 - Liver")
r22.square( CFmode2['liver'] , Rmode2['liver'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r23 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode2 - Lung")
r23.circle( CFmode2['lung'] , Rmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
# ----------
CFmode3 = DataMatrix.loc[('Cout'),('CF')].xs('mode3',level='Mode')
CFmode3 = CFmode3.unstack()
CFALLmode3 = DataMatrix.loc[('Cout'),('CFALL')].xs('mode3',level='Mode')
CFALLmode3 = CFALLmode3.unstack()
Rmode3 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode3',level='Mode')
Rmode3 = Rmode3.unstack()
RALLmode3 = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode3',level='Mode')
RALLmode3 = RALLmode3.unstack()
r31 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode3 - All")
r31.triangle( CFALLmode3['lung'] , RALLmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r32 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode3 - Liver")
r32.square( CFmode3['liver'] , Rmode3['liver'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r33 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode3 - Lung")
r33.circle( CFmode3['lung'] , Rmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
# ----------
CFmode4 = DataMatrix.loc[('Cout'),('CF')].xs('mode4',level='Mode')
CFmode4 = CFmode4.unstack()
CFALLmode4 = DataMatrix.loc[('Cout'),('CFALL')].xs('mode4',level='Mode')
CFALLmode4 = CFALLmode4.unstack()
Rmode4 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode4',level='Mode')
Rmode4 = Rmode4.unstack()
RALLmode4 = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode4',level='Mode')
RALLmode4 = RALLmode4.unstack()
r41 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode4 - All")
r41.triangle( CFALLmode4['lung'] , RALLmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r42 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode4 - Liver")
r42.square( CFmode4['liver'] , Rmode4['liver'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r43 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode4 - Lung")
r43.circle( CFmode4['lung'] , Rmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
# ----------
CFmode5 = DataMatrix.loc[('Cout'),('CF')].xs('mode5',level='Mode')
CFmode5 = CFmode5.unstack()
CFALLmode5 = DataMatrix.loc[('Cout'),('CFALL')].xs('mode5',level='Mode')
CFALLmode5 = CFALLmode5.unstack()
Rmode5 = DataMatrix.loc[('Cout'),('Rsqrt')].xs('mode5',level='Mode')
Rmode5 = Rmode5.unstack()
RALLmode5 = DataMatrix.loc[('Cout'),('RsqrtALL')].xs('mode5',level='Mode')
RALLmode5 = RALLmode5.unstack()
r51 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode5 - All")
r51.triangle( CFALLmode5['lung'] , RALLmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r52 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode5 - Liver")
r52.square( CFmode5['liver'] , Rmode5['liver'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r53 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode5 - Lung")
r53.circle( CFmode5['lung'] , Rmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
p = gridplot([[r11, r21, r31, r41, r51], #, r21, r31, r41, r51],
[r12, r22, r32, r42, r52], #, r22, r32, r42, r52],
[r13, r23, r33, r43, r53]]) #, r23, r33, r43, r53],
show(p)
from bokeh.io import export_png
export_png(p, filename="plots/kp1000/CFR2CoutMulti.png")
Out[26]:
Qec Plots - R2, CF, R2 vs CF:¶
\begin{equation} \normalsize \%idg \approx \frac{C_{tot}}{C_{out}}\ =\ \Bigg\{{\color{blue}{\mathcal{K}_{p}}} + \frac{K_{EC}\ \mathcal{L}_{EC,d}\ {\color{red}{\varphi_{EC}}}}{D_{EC}} {\color{blue}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{EC,d}} + \Bigg\{\frac{K_{M}\ \mathcal{L}_{M,d}\ {\color{blue}{\varphi_{M}}}}{D_{M}} {\color{blue}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{M,d}} + \Bigg\{\frac{K_{EC}\ \mathcal{L}_{EC,d}\ {\color{blue}{\varphi_{S}}}}{D_{EC}} {\color{blue}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{EC,d}} \end{equation}
Qec DataMatrix Statistics:¶
In [27]:
DataMatrix.loc[('Qec')].describe()[(['idg','normIDG','Rsqrt','RsqrtALL','CF','CFALL'])]
Out[27]:
Qec vs R2 Single plot:¶
In [28]:
# Plotting Qec vs R2 all in one plot
p = figure(title="Qec vs R2 ", plot_width=900)
#------------
Linemode1 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = p.line(QEC, Linemode1all['lung'], line_width=3, line_color="tomato", muted_color="tomato", muted_alpha=0.1)
r12 = p.line(QEC, Linemode1['liver'], line_width=3, line_color="purple", muted_color="purple", muted_alpha=0.1)
r13 = p.line(QEC, Linemode1['lung'], line_width=3, line_color="blue", muted_color="blue", muted_alpha=0.1)
# r14 = p.line(COUT, Linemode1['heart'], line_width=3, line_color="green", muted_color="green", muted_alpha=0.1)
#------------
Linemode2 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = p.line(QEC, Linemode2all['lung'], line_width=3, line_color="tomato", line_dash="dashed", muted_color="tomato", muted_alpha=0.1)
r22 = p.line(QEC, Linemode2['liver'], line_width=3, line_color="purple", line_dash="dashed", muted_color="purple", muted_alpha=0.1)
r23 = p.line(QEC, Linemode2['lung'], line_width=3, line_color="blue", line_dash="dashed", muted_color="blue", muted_alpha=0.1)
# r24 = p.line(COUT, Linemode2['heart'], line_width=3, line_color="green", line_dash="dashed", muted_color="green", muted_alpha=0.1)
#------------
Linemode3 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = p.line(QEC, Linemode3all['lung'], line_width=3, line_color="tomato", line_dash="dotted", muted_color="tomato", muted_alpha=0.1)
r32 = p.line(QEC, Linemode3['liver'], line_width=3, line_color="purple", line_dash="dotted", muted_color="purple", muted_alpha=0.1)
r33 = p.line(QEC, Linemode3['lung'], line_width=3, line_color="blue", line_dash="dotted", muted_color="blue", muted_alpha=0.1)
# r34 = p.line(COUT, Linemode3['heart'], line_width=3, line_color="green", line_dash="dotted", muted_color="green", muted_alpha=0.1)
#------------
Linemode4 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = p.line(QEC, Linemode4all['lung'], line_width=3, line_color="tomato", line_dash="dotdash", muted_color="tomato", muted_alpha=0.1)
r42 = p.line(QEC, Linemode4['liver'], line_width=3, line_color="purple", line_dash="dotdash", muted_color="purple", muted_alpha=0.1)
r43 = p.line(QEC, Linemode4['lung'], line_width=3, line_color="blue", line_dash="dotdash", muted_color="blue", muted_alpha=0.1)
# r44 = p.line(COUT, Linemode4['heart'], line_width=3, line_color="green", line_dash="dotdash", muted_color="green", muted_alpha=0.1)
#------------
Linemode5 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = p.line(QEC, Linemode5all['lung'], line_width=3, line_color="tomato", line_dash="dashdot", muted_color="tomato", muted_alpha=0.1)
r52 = p.line(QEC, Linemode5['liver'], line_width=3, line_color="purple", line_dash="dashdot", muted_color="purple", muted_alpha=0.1)
r53 = p.line(QEC, Linemode5['lung'], line_width=3, line_color="blue", line_dash="dashdot", muted_color="blue", muted_alpha=0.1)
# r54 = p.line(COUT, Linemode5['heart'], line_width=3, line_color="green", line_dash="dashdot", muted_color="green", muted_alpha=0.1)
#------------
from bokeh.models import Legend
legend = Legend(items = [
("Mode1 - All Organs", [r11]),
("Mode1 - Liver", [r12]),
("Mode1 - Lung", [r13]),
#("Mode1 - Heart", [r14]),
("Mode2 - All Organs", [r21]),
("Mode2 - Liver", [r22]),
("Mode2 - Lung", [r23]),
#("Mode2 - Heart", [r24]),
("Mode3 - All Organs", [r31]),
("Mode3 - Liver", [r32]),
("Mode3 - Lung", [r33]),
#("Mode3 - Heart", [r34]),
("Mode4 - All Organs", [r41]),
("Mode4 - Liver", [r42]),
("Mode4 - Lung", [r43]),
#("Mode4 - Heart", [r44]),
("Mode5 - All Organs", [r51]),
("Mode5 - Liver", [r52]),
("Mode5 - Lung", [r53])], location = (0, -30))
#("Mode5 - Heart", [r54])], location = (0, -30))
p.add_layout(legend, "right")
p.legend.click_policy="mute"
p.xaxis.axis_label = 'Domain'
p.yaxis.axis_label = 'Values'
show(p)
export_png(p, filename="plots/kp1000/R2Qecsingle.png")
Out[28]:
Qec vs R2 Multi plot:¶
In [29]:
# Plotting Qec vs R2 in seperate plots:
#------------
Linemode1 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = figure( y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - All")
r11.line(QEC, Linemode1all['lung'], line_width=3, line_color="tomato")
r12 = figure( y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Liver")
r12.line(QEC, Linemode1['liver'], line_width=3, line_color="purple")
# r12.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r13 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Lung")
r13.line(QEC, Linemode1['lung'], line_width=3, line_color="blue")
# r14 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Heart")
# r14.line(COUT, Linemode1['heart'], line_width=3, line_color="green")
# #------------
Linemode2 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - All")
r21.line(QEC, Linemode2all['lung'], line_width=3, line_color="tomato")
r22 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Liver")
r22.line(QEC, Linemode2['liver'], line_width=3, line_color="purple")
# r22.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r23 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Lung")
r23.line(QEC, Linemode2['lung'], line_width=3, line_color="blue")
# r24 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Heart")
# r24.line(COUT, Linemode2['heart'], line_width=3, line_color="green")
# #------------
Linemode3 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - All")
r31.line(QEC, Linemode3all['lung'], line_width=3, line_color="tomato")
r32 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Liver")
r32.line(QEC, Linemode3['liver'], line_width=3, line_color="purple")
# r32.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r33 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Lung")
r33.line(QEC, Linemode3['lung'], line_width=3, line_color="blue")
# r34 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Heart")
# r34.line(COUT, Linemode3['heart'], line_width=3, line_color="green")
# #------------
Linemode4 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - All")
r41.line(QEC, Linemode4all['lung'], line_width=3, line_color="tomato")
r42 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Liver")
r42.line(QEC, Linemode4['liver'], line_width=3, line_color="purple")
# r42.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r43 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Lung")
r43.line(QEC, Linemode4['lung'], line_width=3, line_color="blue")
# r44 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Heart")
# r44.line(COUT, Linemode4['heart'], line_width=3, line_color="green")
# #------------
Linemode5 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - All")
r51.line(QEC, Linemode5all['lung'], line_width=3, line_color="tomato")
r52 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Liver")
r52.line(QEC, Linemode5['liver'], line_width=3, line_color="purple")
# r52.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r53 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Lung")
r53.line(QEC, Linemode5['lung'], line_width=3, line_color="blue")
# r54 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Heart")
# r54.line(COUT, Linemode5['heart'], line_width=3, line_color="green")
# #------------
p = gridplot([[r11, r21, r31, r41, r51],
[r12, r22, r32, r42, r52],
[r13, r23, r33, r43, r53]])
show(p)
export_png(p, filename="plots/kp1000/R2QecMulti.png")
Out[29]:
Qec vs CF Single plot:¶
In [30]:
# Plotting Cout vs R2 all in one plot
p = figure(title="Qec vs CF", plot_width=900, y_range= (0,1))
#------------
Linemode1 = DataMatrix.loc[('Qec'),('CF')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Qec'),('CFALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = p.line(QEC, Linemode1all['lung'], line_width=3, line_color="tomato", muted_color="tomato", muted_alpha=0.1)
r12 = p.line(QEC, Linemode1['liver'], line_width=3, line_color="purple", muted_color="purple", muted_alpha=0.1)
r13 = p.line(QEC, Linemode1['lung'], line_width=3, line_color="blue", muted_color="blue", muted_alpha=0.1)
# r14 = p.line(COUT, Linemode1['heart'], line_width=3, line_color="green", muted_color="green", muted_alpha=0.1)
#------------
Linemode2 = DataMatrix.loc[('Qec'),('CF')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Qec'),('CFALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = p.line(QEC, Linemode2all['lung'], line_width=3, line_color="tomato", line_dash="dashed", muted_color="tomato", muted_alpha=0.1)
r22 = p.line(QEC, Linemode2['liver'], line_width=3, line_color="purple", line_dash="dashed", muted_color="purple", muted_alpha=0.1)
r23 = p.line(QEC, Linemode2['lung'], line_width=3, line_color="blue", line_dash="dashed", muted_color="blue", muted_alpha=0.1)
# r24 = p.line(QEC, Linemode2['heart'], line_width=3, line_color="green", line_dash="dashed", muted_color="green", muted_alpha=0.1)
#------------
Linemode3 = DataMatrix.loc[('Qec'),('CF')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Qec'),('CFALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = p.line(QEC, Linemode3all['lung'], line_width=3, line_color="tomato", line_dash="dotted", muted_color="tomato", muted_alpha=0.1)
r32 = p.line(QEC, Linemode3['liver'], line_width=3, line_color="purple", line_dash="dotted", muted_color="purple", muted_alpha=0.1)
r33 = p.line(QEC, Linemode3['lung'], line_width=3, line_color="blue", line_dash="dotted", muted_color="blue", muted_alpha=0.1)
# r34 = p.line(QEC, Linemode3['heart'], line_width=3, line_color="green", line_dash="dotted", muted_color="green", muted_alpha=0.1)
#------------
Linemode4 = DataMatrix.loc[('Qec'),('CF')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Qec'),('CFALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = p.line(QEC, Linemode4all['lung'], line_width=3, line_color="tomato", line_dash="dotdash", muted_color="tomato", muted_alpha=0.1)
r42 = p.line(QEC, Linemode4['liver'], line_width=3, line_color="purple", line_dash="dotdash", muted_color="purple", muted_alpha=0.1)
r43 = p.line(QEC, Linemode4['lung'], line_width=3, line_color="blue", line_dash="dotdash", muted_color="blue", muted_alpha=0.1)
# r44 = p.line(QEC, Linemode4['heart'], line_width=3, line_color="green", line_dash="dotdash", muted_color="green", muted_alpha=0.1)
#------------
Linemode5 = DataMatrix.loc[('Qec'),('CF')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Qec'),('CFALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = p.line(QEC, Linemode5all['lung'], line_width=3, line_color="tomato", line_dash="dashdot", muted_color="tomato", muted_alpha=0.1)
r52 = p.line(QEC, Linemode5['liver'], line_width=3, line_color="purple", line_dash="dashdot", muted_color="purple", muted_alpha=0.1)
r53 = p.line(QEC, Linemode5['lung'], line_width=3, line_color="blue", line_dash="dashdot", muted_color="blue", muted_alpha=0.1)
# r54 = p.line(QEC, Linemode5['heart'], line_width=3, line_color="green", line_dash="dashdot", muted_color="green", muted_alpha=0.1)
#------------
# from bokeh.models import Legend
legend = Legend(items = [
("Mode1 - All Organs", [r11]),
("Mode1 - Liver", [r12]),
("Mode1 - Lung", [r13]),
# ("Mode1 - Heart", [r14]),
("Mode2 - All Organs", [r21]),
("Mode2 - Liver", [r22]),
("Mode2 - Lung", [r23]),
# ("Mode2 - Heart", [r24]),
("Mode3 - All Organs", [r31]),
("Mode3 - Liver", [r32]),
("Mode3 - Lung", [r33]),
# ("Mode3 - Heart", [r34]),
("Mode4 - All Organs", [r41]),
("Mode4 - Liver", [r42]),
("Mode4 - Lung", [r43]),
# ("Mode4 - Heart", [r44]),
("Mode5 - All Organs", [r51]),
("Mode5 - Liver", [r52]),
("Mode5 - Lung", [r53])], location = (0, -30))
# ("Mode5 - Heart", [r54])
p.add_layout(legend, "right")
p.legend.click_policy="mute"
p.xaxis.axis_label = 'Qec Range'
p.yaxis.axis_label = 'CF'
show(p)
export_png(p, filename="plots/kp1000/CFQecSingle.png")
Out[30]:
Qec vs CF Multi plot:¶
In [31]:
# Plotting Cout vs R2 in seperate plots:
#------------
Linemode1 = DataMatrix.loc[('Qec'),('CF')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Qec'),('CFALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - All")
r11.line(QEC, Linemode1all['lung'], line_width=3, line_color="tomato")
r12 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Liver")
r12.line(QEC, Linemode1['liver'], line_width=3, line_color="purple")
# r12.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r13 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Lung")
r13.line(QEC, Linemode1['lung'], line_width=3, line_color="blue")
# r14 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode1 - Heart")
# r14.line(COUT, Linemode1['heart'], line_width=3, line_color="green")
# #------------
Linemode2 = DataMatrix.loc[('Qec'),('CF')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Qec'),('CFALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = figure( y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - All")
r21.line(QEC, Linemode2all['lung'], line_width=3, line_color="tomato")
r22 = figure(y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode2 - Liver")
r22.line(QEC, Linemode2['liver'], line_width=3, line_color="purple")
# r22.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r23 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Lung")
r23.line(QEC, Linemode2['lung'], line_width=3, line_color="blue")
# r24 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode2 - Heart")
# r24.line(COUT, Linemode2['heart'], line_width=3, line_color="green")
# #------------
Linemode3 = DataMatrix.loc[('Qec'),('CF')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Qec'),('CFALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - All")
r31.line(QEC, Linemode3all['lung'], line_width=3, line_color="tomato")
r32 = figure(y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode3 - Liver")
r32.line(QEC, Linemode3['liver'], line_width=3, line_color="purple")
# r32.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r33 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Lung")
r33.line(QEC, Linemode3['lung'], line_width=3, line_color="blue")
# r34 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode3 - Heart")
# r34.line(COUT, Linemode3['heart'], line_width=3, line_color="green")
# #------------
Linemode4 = DataMatrix.loc[('Qec'),('CF')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Qec'),('CFALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - All")
r41.line(QEC, Linemode4all['lung'], line_width=3, line_color="tomato")
r42 = figure(y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode4 - Liver")
r42.line(QEC, Linemode4['liver'], line_width=3, line_color="purple")
# r42.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r43 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Lung")
r43.line(QEC, Linemode4['lung'], line_width=3, line_color="blue")
# r44 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode4 - Heart")
# r44.line(COUT, Linemode4['heart'], line_width=3, line_color="green")
# #------------
Linemode5 = DataMatrix.loc[('Qec'),('CF')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Qec'),('CFALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - All")
r51.line(QEC, Linemode5all['lung'], line_width=3, line_color="tomato")
r52 = figure(y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode5 - Liver")
r52.line(QEC, Linemode5['liver'], line_width=3, line_color="purple")
# r52.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r53 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Lung")
r53.line(QEC, Linemode5['lung'], line_width=3, line_color="blue")
# r54 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode5 - Heart")
# r54.line(COUT, Linemode5['heart'], line_width=3, line_color="green")
# #------------
p = gridplot([[r11, r21, r31, r41, r51],
[r12, r22, r32, r42, r52],
[r13, r23, r33, r43, r53]])
show(p)
export_png(p, filename="plots/kp1000/CFQecMulti.png")
Out[31]:
R2 vs CF for Qec single plot:¶
In [32]:
# Plotting R2 vs CF for Qec all in one plot
p = figure(title="R2 vs CF ", plot_width=900, x_range = (0, 1), y_range = (0, 1)) #
#------------
CFmode1 = DataMatrix.loc[('Qec'),('CF')].xs('mode1',level='Mode')
CFmode1 = CFmode1.unstack()
CFALLmode1 = DataMatrix.loc[('Qec'),('CFALL')].xs('mode1',level='Mode')
CFALLmode1 = CFALLmode1.unstack()
Rmode1 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode1',level='Mode')
Rmode1 = Rmode1.unstack()
RALLmode1 = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode1',level='Mode')
RALLmode1 = RALLmode1.unstack()
r11 = p.triangle( CFALLmode1['lung'] , RALLmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r12 = p.square( CFmode1['liver'] , Rmode1['liver'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r13 = p.circle( CFmode1['lung'] , Rmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
#------------
CFmode2 = DataMatrix.loc[('Qec'),('CF')].xs('mode2',level='Mode')
CFmode2 = CFmode2.unstack()
CFALLmode2 = DataMatrix.loc[('Qec'),('CFALL')].xs('mode2',level='Mode')
CFALLmode2 = CFALLmode2.unstack()
Rmode2 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode2',level='Mode')
Rmode2 = Rmode2.unstack()
RALLmode2 = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode2',level='Mode')
RALLmode2 = RALLmode2.unstack()
r21 = p.triangle( CFALLmode2['lung'] , RALLmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r22 = p.square( CFmode2['liver'] , Rmode2['liver'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r23 = p.circle( CFmode2['lung'] , Rmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
#------------
CFmode3 = DataMatrix.loc[('Qec'),('CF')].xs('mode3',level='Mode')
CFmode3 = CFmode3.unstack()
CFALLmode3 = DataMatrix.loc[('Qec'),('CFALL')].xs('mode3',level='Mode')
CFALLmode3 = CFALLmode3.unstack()
Rmode3 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode3',level='Mode')
Rmode3 = Rmode3.unstack()
RALLmode3 = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode3',level='Mode')
RALLmode3 = RALLmode3.unstack()
r31 = p.triangle( CFALLmode3['lung'] , RALLmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r32 = p.square( CFmode3['liver'] , Rmode3['liver'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r33 = p.circle( CFmode3['lung'] , Rmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
#------------
CFmode4 = DataMatrix.loc[('Qec'),('CF')].xs('mode4',level='Mode')
CFmode4 = CFmode4.unstack()
CFALLmode4 = DataMatrix.loc[('Qec'),('CFALL')].xs('mode4',level='Mode')
CFALLmode4 = CFALLmode4.unstack()
Rmode4 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode4',level='Mode')
Rmode4 = Rmode4.unstack()
RALLmode4 = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode4',level='Mode')
RALLmode4 = RALLmode4.unstack()
r41 = p.triangle( CFALLmode4['lung'] , RALLmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r42 = p.square( CFmode4['liver'] , Rmode4['liver'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r43 = p.circle( CFmode4['lung'] , Rmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
#------------
CFmode5 = DataMatrix.loc[('Qec'),('CF')].xs('mode5',level='Mode')
CFmode5 = CFmode5.unstack()
CFALLmode5 = DataMatrix.loc[('Qec'),('CFALL')].xs('mode5',level='Mode')
CFALLmode5 = CFALLmode5.unstack()
Rmode5 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode5',level='Mode')
Rmode5 = Rmode5.unstack()
RALLmode5 = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode5',level='Mode')
RALLmode5 = RALLmode5.unstack()
r51 = p.triangle( CFALLmode5['lung'] , RALLmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r52 = p.square( CFmode5['liver'] , Rmode5['liver'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r53 = p.circle( CFmode5['lung'] , Rmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
legend = Legend(items = [
("Mode1 - All Organs", [r11]),
("Mode1 - Liver", [r12]),
("Mode1 - Lung", [r13]),
("Mode2 - All Organs", [r21]),
("Mode2 - Liver", [r22]),
("Mode2 - Lung", [r23]),
("Mode3 - All Organs", [r31]),
("Mode3 - Liver", [r32]),
("Mode3 - Lung", [r33]),
("Mode4 - All Organs", [r41]),
("Mode4 - Liver", [r42]),
("Mode4 - Lung", [r43]),
("Mode5 - All Organs", [r51]),
("Mode5 - Liver", [r52]),
("Mode5 - Lung", [r53])], location = (0, -30))
p.add_layout(legend, "right")
p.legend.click_policy="mute"
p.xaxis.axis_label = 'CF'
p.yaxis.axis_label = 'R2'
show(p)
export_png(p, filename="plots/kp1000/CFR2QecSingle.png")
Out[32]:
R2 vs CF for Qec Miltiple plot:¶
In [33]:
# Plotting Cout vs R2 in seperate plots:
#------------
p = figure(title="R2 vs CF ", plot_width=900, x_range = (0, 1), y_range = (0, 1)) #
#------------
CFmode1 = DataMatrix.loc[('Qec'),('CF')].xs('mode1',level='Mode')
CFmode1 = CFmode1.unstack()
CFALLmode1 = DataMatrix.loc[('Qec'),('CFALL')].xs('mode1',level='Mode')
CFALLmode1 = CFALLmode1.unstack()
Rmode1 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode1',level='Mode')
Rmode1 = Rmode1.unstack()
RALLmode1 = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode1',level='Mode')
RALLmode1 = RALLmode1.unstack()
r11 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode1 - All")
r11.triangle( CFALLmode1['lung'] , RALLmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r12 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode1 - Liver")
r12.square( CFmode1['liver'] , Rmode1['liver'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r13 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode1 - Lung")
r13.circle( CFmode1['lung'] , Rmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
# ----------
CFmode2 = DataMatrix.loc[('Qec'),('CF')].xs('mode2',level='Mode')
CFmode2 = CFmode2.unstack()
CFALLmode2 = DataMatrix.loc[('Qec'),('CFALL')].xs('mode2',level='Mode')
CFALLmode2 = CFALLmode2.unstack()
Rmode2 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode2',level='Mode')
Rmode2 = Rmode2.unstack()
RALLmode2 = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode2',level='Mode')
RALLmode2 = RALLmode2.unstack()
r21 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode2 - All")
r21.triangle( CFALLmode2['lung'] , RALLmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r22 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode2 - Liver")
r22.square( CFmode2['liver'] , Rmode2['liver'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r23 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode2 - Lung")
r23.circle( CFmode2['lung'] , Rmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
# ----------
CFmode3 = DataMatrix.loc[('Qec'),('CF')].xs('mode3',level='Mode')
CFmode3 = CFmode3.unstack()
CFALLmode3 = DataMatrix.loc[('Qec'),('CFALL')].xs('mode3',level='Mode')
CFALLmode3 = CFALLmode3.unstack()
Rmode3 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode3',level='Mode')
Rmode3 = Rmode3.unstack()
RALLmode3 = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode3',level='Mode')
RALLmode3 = RALLmode3.unstack()
r31 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode3 - All")
r31.triangle( CFALLmode3['lung'] , RALLmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r32 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode3 - Liver")
r32.square( CFmode3['liver'] , Rmode3['liver'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r33 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode3 - Lung")
r33.circle( CFmode3['lung'] , Rmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
# ----------
CFmode4 = DataMatrix.loc[('Qec'),('CF')].xs('mode4',level='Mode')
CFmode4 = CFmode4.unstack()
CFALLmode4 = DataMatrix.loc[('Qec'),('CFALL')].xs('mode4',level='Mode')
CFALLmode4 = CFALLmode4.unstack()
Rmode4 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode4',level='Mode')
Rmode4 = Rmode4.unstack()
RALLmode4 = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode4',level='Mode')
RALLmode4 = RALLmode4.unstack()
r41 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode4 - All")
r41.triangle( CFALLmode4['lung'] , RALLmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r42 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode4 - Liver")
r42.square( CFmode4['liver'] , Rmode4['liver'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r43 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode4 - Lung")
r43.circle( CFmode4['lung'] , Rmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
# ----------
CFmode5 = DataMatrix.loc[('Qec'),('CF')].xs('mode5',level='Mode')
CFmode5 = CFmode5.unstack()
CFALLmode5 = DataMatrix.loc[('Qec'),('CFALL')].xs('mode5',level='Mode')
CFALLmode5 = CFALLmode5.unstack()
Rmode5 = DataMatrix.loc[('Qec'),('Rsqrt')].xs('mode5',level='Mode')
Rmode5 = Rmode5.unstack()
RALLmode5 = DataMatrix.loc[('Qec'),('RsqrtALL')].xs('mode5',level='Mode')
RALLmode5 = RALLmode5.unstack()
r51 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode5 - All")
r51.triangle( CFALLmode5['lung'] , RALLmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r52 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode5 - Liver")
r52.square( CFmode5['liver'] , Rmode5['liver'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r53 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode5 - Lung")
r53.circle( CFmode5['lung'] , Rmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
p = gridplot([[r11, r21, r31, r41, r51], #, r21, r31, r41, r51],
[r12, r22, r32, r42, r52], #, r22, r32, r42, r52],
[r13, r23, r33, r43, r53]]) #, r23, r33, r43, r53],
show(p)
export_png(p, filename="plots/kp1000/CFR2QecMulti.png")
Out[33]:
Qm Plots - R2, CF, R2 vs CF:¶
\begin{equation} \normalsize \%idg \approx \frac{C_{tot}}{C_{out}}\ =\ \Bigg\{{\color{blue}{\mathcal{K}_{p}}} + \frac{K_{EC}\ \mathcal{L}_{EC,d}\ {\color{blue}{\varphi_{EC}}}}{D_{EC}} {\color{blue}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{EC,d}} + \Bigg\{\frac{K_{M}\ \mathcal{L}_{M,d}\ {\color{red}{\varphi_{M}}}}{D_{M}} {\color{blue}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{M,d}} + \Bigg\{\frac{K_{EC}\ \mathcal{L}_{EC,d}\ {\color{blue}{\varphi_{S}}}}{D_{EC}} {\color{blue}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{EC,d}} \end{equation}
Qec DataMatrix Statistics:¶
In [34]:
DataMatrix.loc[('Qm')].describe()[(['idg','normIDG','Rsqrt','RsqrtALL','CF','CFALL'])]
Out[34]:
Qm vs R2 Single plot:¶
In [35]:
# Plotting Qm vs R2 all in one plot
p = figure(title="Qm vs R2 ", plot_width=900)
#------------
Linemode1 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = p.line(QM, Linemode1all['lung'], line_width=3, line_color="tomato", muted_color="tomato", muted_alpha=0.1)
r12 = p.line(QM, Linemode1['liver'], line_width=3, line_color="purple", muted_color="purple", muted_alpha=0.1)
r13 = p.line(QM, Linemode1['lung'], line_width=3, line_color="blue", muted_color="blue", muted_alpha=0.1)
# r14 = p.line(COUT, Linemode1['heart'], line_width=3, line_color="green", muted_color="green", muted_alpha=0.1)
#------------
Linemode2 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = p.line(QM, Linemode2all['lung'], line_width=3, line_color="tomato", line_dash="dashed", muted_color="tomato", muted_alpha=0.1)
r22 = p.line(QM, Linemode2['liver'], line_width=3, line_color="purple", line_dash="dashed", muted_color="purple", muted_alpha=0.1)
r23 = p.line(QM, Linemode2['lung'], line_width=3, line_color="blue", line_dash="dashed", muted_color="blue", muted_alpha=0.1)
# r24 = p.line(COUT, Linemode2['heart'], line_width=3, line_color="green", line_dash="dashed", muted_color="green", muted_alpha=0.1)
#------------
Linemode3 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = p.line(QM, Linemode3all['lung'], line_width=3, line_color="tomato", line_dash="dotted", muted_color="tomato", muted_alpha=0.1)
r32 = p.line(QM, Linemode3['liver'], line_width=3, line_color="purple", line_dash="dotted", muted_color="purple", muted_alpha=0.1)
r33 = p.line(QM, Linemode3['lung'], line_width=3, line_color="blue", line_dash="dotted", muted_color="blue", muted_alpha=0.1)
# r34 = p.line(COUT, Linemode3['heart'], line_width=3, line_color="green", line_dash="dotted", muted_color="green", muted_alpha=0.1)
#------------
Linemode4 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = p.line(QM, Linemode4all['lung'], line_width=3, line_color="tomato", line_dash="dotdash", muted_color="tomato", muted_alpha=0.1)
r42 = p.line(QM, Linemode4['liver'], line_width=3, line_color="purple", line_dash="dotdash", muted_color="purple", muted_alpha=0.1)
r43 = p.line(QM, Linemode4['lung'], line_width=3, line_color="blue", line_dash="dotdash", muted_color="blue", muted_alpha=0.1)
# r44 = p.line(COUT, Linemode4['heart'], line_width=3, line_color="green", line_dash="dotdash", muted_color="green", muted_alpha=0.1)
#------------
Linemode5 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = p.line(QM, Linemode5all['lung'], line_width=3, line_color="tomato", line_dash="dashdot", muted_color="tomato", muted_alpha=0.1)
r52 = p.line(QM, Linemode5['liver'], line_width=3, line_color="purple", line_dash="dashdot", muted_color="purple", muted_alpha=0.1)
r53 = p.line(QM, Linemode5['lung'], line_width=3, line_color="blue", line_dash="dashdot", muted_color="blue", muted_alpha=0.1)
# r54 = p.line(COUT, Linemode5['heart'], line_width=3, line_color="green", line_dash="dashdot", muted_color="green", muted_alpha=0.1)
#------------
from bokeh.models import Legend
legend = Legend(items = [
("Mode1 - All Organs", [r11]),
("Mode1 - Liver", [r12]),
("Mode1 - Lung", [r13]),
#("Mode1 - Heart", [r14]),
("Mode2 - All Organs", [r21]),
("Mode2 - Liver", [r22]),
("Mode2 - Lung", [r23]),
#("Mode2 - Heart", [r24]),
("Mode3 - All Organs", [r31]),
("Mode3 - Liver", [r32]),
("Mode3 - Lung", [r33]),
#("Mode3 - Heart", [r34]),
("Mode4 - All Organs", [r41]),
("Mode4 - Liver", [r42]),
("Mode4 - Lung", [r43]),
#("Mode4 - Heart", [r44]),
("Mode5 - All Organs", [r51]),
("Mode5 - Liver", [r52]),
("Mode5 - Lung", [r53])], location = (0, -30))
#("Mode5 - Heart", [r54])], location = (0, -30))
p.add_layout(legend, "right")
p.legend.click_policy="mute"
p.xaxis.axis_label = 'Qm Range'
p.yaxis.axis_label = 'R2'
show(p)
export_png(p, filename="plots/kp1000/R2QmSingle.png")
Out[35]:
In [36]:
Linemode1 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1
Out[36]:
Qm vs R2 Multi plot:¶
In [37]:
# Plotting Qm vs R2 in seperate plots:
#------------
Linemode1 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = figure( y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - All")
r11.line(QM, Linemode1all['lung'], line_width=3, line_color="tomato")
r12 = figure( y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Liver")
r12.line(QM, Linemode1['liver'], line_width=3, line_color="purple")
# r12.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r13 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Lung")
r13.line(QM, Linemode1['lung'], line_width=3, line_color="blue")
# r14 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Heart")
# r14.line(COUT, Linemode1['heart'], line_width=3, line_color="green")
# #------------
Linemode2 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - All")
r21.line(QM, Linemode2all['lung'], line_width=3, line_color="tomato")
r22 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Liver")
r22.line(QM, Linemode2['liver'], line_width=3, line_color="purple")
# r22.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r23 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Lung")
r23.line(QM, Linemode2['lung'], line_width=3, line_color="blue")
# r24 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Heart")
# r24.line(COUT, Linemode2['heart'], line_width=3, line_color="green")
# #------------
Linemode3 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - All")
r31.line(QM, Linemode3all['lung'], line_width=3, line_color="tomato")
r32 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Liver")
r32.line(QM, Linemode3['liver'], line_width=3, line_color="purple")
# r32.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r33 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Lung")
r33.line(QM, Linemode3['lung'], line_width=3, line_color="blue")
# r34 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Heart")
# r34.line(COUT, Linemode3['heart'], line_width=3, line_color="green")
# #------------
Linemode4 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - All")
r41.line(QM, Linemode4all['lung'], line_width=3, line_color="tomato")
r42 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Liver")
r42.line(QM, Linemode4['liver'], line_width=3, line_color="purple")
# r42.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r43 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Lung")
r43.line(QM, Linemode4['lung'], line_width=3, line_color="blue")
# r44 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Heart")
# r44.line(COUT, Linemode4['heart'], line_width=3, line_color="green")
# #------------
Linemode5 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - All")
r51.line(QM, Linemode5all['lung'], line_width=3, line_color="tomato")
r52 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Liver")
r52.line(QM, Linemode5['liver'], line_width=3, line_color="purple")
# r52.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r53 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Lung")
r53.line(QM, Linemode5['lung'], line_width=3, line_color="blue")
# r54 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Heart")
# r54.line(COUT, Linemode5['heart'], line_width=3, line_color="green")
# #------------
p = gridplot([[r11, r21, r31, r41, r51],
[r12, r22, r32, r42, r52],
[r13, r23, r33, r43, r53]])
show(p)
export_png(p, filename="plots/kp1000/R2QmMulti.png")
Out[37]:
Qm vs CF Single plot:¶
In [38]:
# Plotting Cout vs R2 all in one plot
p = figure(title="Qm vs CF", plot_width=900, y_range= (0,1))
#------------
Linemode1 = DataMatrix.loc[('Qm'),('CF')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Qm'),('CFALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = p.line(QM, Linemode1all['lung'], line_width=3, line_color="tomato", muted_color="tomato", muted_alpha=0.1)
r12 = p.line(QM, Linemode1['liver'], line_width=3, line_color="purple", muted_color="purple", muted_alpha=0.1)
r13 = p.line(QM, Linemode1['lung'], line_width=3, line_color="blue", muted_color="blue", muted_alpha=0.1)
# r14 = p.line(COUT, Linemode1['heart'], line_width=3, line_color="green", muted_color="green", muted_alpha=0.1)
#------------
Linemode2 = DataMatrix.loc[('Qm'),('CF')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Qm'),('CFALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = p.line(QM, Linemode2all['lung'], line_width=3, line_color="tomato", line_dash="dashed", muted_color="tomato", muted_alpha=0.1)
r22 = p.line(QM, Linemode2['liver'], line_width=3, line_color="purple", line_dash="dashed", muted_color="purple", muted_alpha=0.1)
r23 = p.line(QM, Linemode2['lung'], line_width=3, line_color="blue", line_dash="dashed", muted_color="blue", muted_alpha=0.1)
# r24 = p.line(QEC, Linemode2['heart'], line_width=3, line_color="green", line_dash="dashed", muted_color="green", muted_alpha=0.1)
#------------
Linemode3 = DataMatrix.loc[('Qm'),('CF')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Qm'),('CFALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = p.line(QM, Linemode3all['lung'], line_width=3, line_color="tomato", line_dash="dotted", muted_color="tomato", muted_alpha=0.1)
r32 = p.line(QM, Linemode3['liver'], line_width=3, line_color="purple", line_dash="dotted", muted_color="purple", muted_alpha=0.1)
r33 = p.line(QM, Linemode3['lung'], line_width=3, line_color="blue", line_dash="dotted", muted_color="blue", muted_alpha=0.1)
# r34 = p.line(QEC, Linemode3['heart'], line_width=3, line_color="green", line_dash="dotted", muted_color="green", muted_alpha=0.1)
#------------
Linemode4 = DataMatrix.loc[('Qm'),('CF')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Qm'),('CFALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = p.line(QM, Linemode4all['lung'], line_width=3, line_color="tomato", line_dash="dotdash", muted_color="tomato", muted_alpha=0.1)
r42 = p.line(QM, Linemode4['liver'], line_width=3, line_color="purple", line_dash="dotdash", muted_color="purple", muted_alpha=0.1)
r43 = p.line(QM, Linemode4['lung'], line_width=3, line_color="blue", line_dash="dotdash", muted_color="blue", muted_alpha=0.1)
# r44 = p.line(QEC, Linemode4['heart'], line_width=3, line_color="green", line_dash="dotdash", muted_color="green", muted_alpha=0.1)
#------------
Linemode5 = DataMatrix.loc[('Qm'),('CF')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Qm'),('CFALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = p.line(QM, Linemode5all['lung'], line_width=3, line_color="tomato", line_dash="dashdot", muted_color="tomato", muted_alpha=0.1)
r52 = p.line(QM, Linemode5['liver'], line_width=3, line_color="purple", line_dash="dashdot", muted_color="purple", muted_alpha=0.1)
r53 = p.line(QM, Linemode5['lung'], line_width=3, line_color="blue", line_dash="dashdot", muted_color="blue", muted_alpha=0.1)
# r54 = p.line(QEC, Linemode5['heart'], line_width=3, line_color="green", line_dash="dashdot", muted_color="green", muted_alpha=0.1)
#------------
# from bokeh.models import Legend
legend = Legend(items = [
("Mode1 - All Organs", [r11]),
("Mode1 - Liver", [r12]),
("Mode1 - Lung", [r13]),
# ("Mode1 - Heart", [r14]),
("Mode2 - All Organs", [r21]),
("Mode2 - Liver", [r22]),
("Mode2 - Lung", [r23]),
# ("Mode2 - Heart", [r24]),
("Mode3 - All Organs", [r31]),
("Mode3 - Liver", [r32]),
("Mode3 - Lung", [r33]),
# ("Mode3 - Heart", [r34]),
("Mode4 - All Organs", [r41]),
("Mode4 - Liver", [r42]),
("Mode4 - Lung", [r43]),
# ("Mode4 - Heart", [r44]),
("Mode5 - All Organs", [r51]),
("Mode5 - Liver", [r52]),
("Mode5 - Lung", [r53])], location = (0, -30))
# ("Mode5 - Heart", [r54])
p.add_layout(legend, "right")
p.legend.click_policy="mute"
p.xaxis.axis_label = 'Qm Range'
p.yaxis.axis_label = 'CF'
show(p)
export_png(p, filename="plots/kp1000/CFQmSingle.png")
Out[38]:
Qm vs CF Multi plot:¶
In [39]:
# Plotting Qm vs R2 in seperate plots:
#------------
Linemode1 = DataMatrix.loc[('Qm'),('CF')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Qm'),('CFALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - All")
r11.line(QM, Linemode1all['lung'], line_width=3, line_color="tomato")
r12 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Liver")
r12.line(QM, Linemode1['liver'], line_width=3, line_color="purple")
# r12.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r13 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Lung")
r13.line(QM, Linemode1['lung'], line_width=3, line_color="blue")
# r14 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode1 - Heart")
# r14.line(COUT, Linemode1['heart'], line_width=3, line_color="green")
# #------------
Linemode2 = DataMatrix.loc[('Qm'),('CF')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Qm'),('CFALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = figure( y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - All")
r21.line(QM, Linemode2all['lung'], line_width=3, line_color="tomato")
r22 = figure(y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode2 - Liver")
r22.line(QM, Linemode2['liver'], line_width=3, line_color="purple")
# r22.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r23 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Lung")
r23.line(QM, Linemode2['lung'], line_width=3, line_color="blue")
# r24 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode2 - Heart")
# r24.line(COUT, Linemode2['heart'], line_width=3, line_color="green")
# #------------
Linemode3 = DataMatrix.loc[('Qm'),('CF')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Qm'),('CFALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - All")
r31.line(QM, Linemode3all['lung'], line_width=3, line_color="tomato")
r32 = figure(y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode3 - Liver")
r32.line(QM, Linemode3['liver'], line_width=3, line_color="purple")
# r32.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r33 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Lung")
r33.line(QM, Linemode3['lung'], line_width=3, line_color="blue")
# r34 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode3 - Heart")
# r34.line(COUT, Linemode3['heart'], line_width=3, line_color="green")
# #------------
Linemode4 = DataMatrix.loc[('Qm'),('CF')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Qm'),('CFALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - All")
r41.line(QM, Linemode4all['lung'], line_width=3, line_color="tomato")
r42 = figure(y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode4 - Liver")
r42.line(QM, Linemode4['liver'], line_width=3, line_color="purple")
# r42.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r43 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Lung")
r43.line(QM, Linemode4['lung'], line_width=3, line_color="blue")
# r44 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode4 - Heart")
# r44.line(COUT, Linemode4['heart'], line_width=3, line_color="green")
# #------------
Linemode5 = DataMatrix.loc[('Qm'),('CF')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Qm'),('CFALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - All")
r51.line(QM, Linemode5all['lung'], line_width=3, line_color="tomato")
r52 = figure(y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode5 - Liver")
r52.line(QM, Linemode5['liver'], line_width=3, line_color="purple")
# r52.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r53 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Lung")
r53.line(QM, Linemode5['lung'], line_width=3, line_color="blue")
# r54 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode5 - Heart")
# r54.line(COUT, Linemode5['heart'], line_width=3, line_color="green")
# #------------
p = gridplot([[r11, r21, r31, r41, r51],
[r12, r22, r32, r42, r52],
[r13, r23, r33, r43, r53]])
show(p)
export_png(p, filename="plots/kp1000/CFQmMulti.png")
Out[39]:
R2 vs CF for Qm single plot:¶
In [40]:
# Plotting R2 vs CF for Qm all in one plot
p = figure(title="R2 vs CF ", plot_width=900, x_range = (0, 1), y_range = (0, 1)) #
#------------
CFmode1 = DataMatrix.loc[('Qm'),('CF')].xs('mode1',level='Mode')
CFmode1 = CFmode1.unstack()
CFALLmode1 = DataMatrix.loc[('Qm'),('CFALL')].xs('mode1',level='Mode')
CFALLmode1 = CFALLmode1.unstack()
Rmode1 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode1',level='Mode')
Rmode1 = Rmode1.unstack()
RALLmode1 = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode1',level='Mode')
RALLmode1 = RALLmode1.unstack()
r11 = p.triangle( CFALLmode1['lung'] , RALLmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r12 = p.square( CFmode1['liver'] , Rmode1['liver'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r13 = p.circle( CFmode1['lung'] , Rmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
#------------
CFmode2 = DataMatrix.loc[('Qm'),('CF')].xs('mode2',level='Mode')
CFmode2 = CFmode2.unstack()
CFALLmode2 = DataMatrix.loc[('Qm'),('CFALL')].xs('mode2',level='Mode')
CFALLmode2 = CFALLmode2.unstack()
Rmode2 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode2',level='Mode')
Rmode2 = Rmode2.unstack()
RALLmode2 = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode2',level='Mode')
RALLmode2 = RALLmode2.unstack()
r21 = p.triangle( CFALLmode2['lung'] , RALLmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r22 = p.square( CFmode2['liver'] , Rmode2['liver'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r23 = p.circle( CFmode2['lung'] , Rmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
#------------
CFmode3 = DataMatrix.loc[('Qm'),('CF')].xs('mode3',level='Mode')
CFmode3 = CFmode3.unstack()
CFALLmode3 = DataMatrix.loc[('Qm'),('CFALL')].xs('mode3',level='Mode')
CFALLmode3 = CFALLmode3.unstack()
Rmode3 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode3',level='Mode')
Rmode3 = Rmode3.unstack()
RALLmode3 = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode3',level='Mode')
RALLmode3 = RALLmode3.unstack()
r31 = p.triangle( CFALLmode3['lung'] , RALLmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r32 = p.square( CFmode3['liver'] , Rmode3['liver'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r33 = p.circle( CFmode3['lung'] , Rmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
#------------
CFmode4 = DataMatrix.loc[('Qm'),('CF')].xs('mode4',level='Mode')
CFmode4 = CFmode4.unstack()
CFALLmode4 = DataMatrix.loc[('Qm'),('CFALL')].xs('mode4',level='Mode')
CFALLmode4 = CFALLmode4.unstack()
Rmode4 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode4',level='Mode')
Rmode4 = Rmode4.unstack()
RALLmode4 = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode4',level='Mode')
RALLmode4 = RALLmode4.unstack()
r41 = p.triangle( CFALLmode4['lung'] , RALLmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r42 = p.square( CFmode4['liver'] , Rmode4['liver'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r43 = p.circle( CFmode4['lung'] , Rmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
#------------
CFmode5 = DataMatrix.loc[('Qm'),('CF')].xs('mode5',level='Mode')
CFmode5 = CFmode5.unstack()
CFALLmode5 = DataMatrix.loc[('Qm'),('CFALL')].xs('mode5',level='Mode')
CFALLmode5 = CFALLmode5.unstack()
Rmode5 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode5',level='Mode')
Rmode5 = Rmode5.unstack()
RALLmode5 = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode5',level='Mode')
RALLmode5 = RALLmode5.unstack()
r51 = p.triangle( CFALLmode5['lung'] , RALLmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r52 = p.square( CFmode5['liver'] , Rmode5['liver'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r53 = p.circle( CFmode5['lung'] , Rmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
legend = Legend(items = [
("Mode1 - All Organs", [r11]),
("Mode1 - Liver", [r12]),
("Mode1 - Lung", [r13]),
("Mode2 - All Organs", [r21]),
("Mode2 - Liver", [r22]),
("Mode2 - Lung", [r23]),
("Mode3 - All Organs", [r31]),
("Mode3 - Liver", [r32]),
("Mode3 - Lung", [r33]),
("Mode4 - All Organs", [r41]),
("Mode4 - Liver", [r42]),
("Mode4 - Lung", [r43]),
("Mode5 - All Organs", [r51]),
("Mode5 - Liver", [r52]),
("Mode5 - Lung", [r53])], location = (0, -30))
p.add_layout(legend, "right")
p.legend.click_policy="mute"
p.xaxis.axis_label = 'CF'
p.yaxis.axis_label = 'R2'
show(p)
export_png(p, filename="plots/kp1000/CFR2QmSingle.png")
Out[40]:
R2 vs CF for Qm Miltiple plot:¶
In [41]:
# Plotting Qm vs R2 in seperate plots:
#------------
p = figure(title="R2 vs CF ", plot_width=900, x_range = (0, 1), y_range = (0, 1)) #
#------------
CFmode1 = DataMatrix.loc[('Qm'),('CF')].xs('mode1',level='Mode')
CFmode1 = CFmode1.unstack()
CFALLmode1 = DataMatrix.loc[('Qm'),('CFALL')].xs('mode1',level='Mode')
CFALLmode1 = CFALLmode1.unstack()
Rmode1 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode1',level='Mode')
Rmode1 = Rmode1.unstack()
RALLmode1 = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode1',level='Mode')
RALLmode1 = RALLmode1.unstack()
r11 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode1 - All")
r11.triangle( CFALLmode1['lung'] , RALLmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r12 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode1 - Liver")
r12.square( CFmode1['liver'] , Rmode1['liver'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r13 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode1 - Lung")
r13.circle( CFmode1['lung'] , Rmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
# ----------
CFmode2 = DataMatrix.loc[('Qm'),('CF')].xs('mode2',level='Mode')
CFmode2 = CFmode2.unstack()
CFALLmode2 = DataMatrix.loc[('Qm'),('CFALL')].xs('mode2',level='Mode')
CFALLmode2 = CFALLmode2.unstack()
Rmode2 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode2',level='Mode')
Rmode2 = Rmode2.unstack()
RALLmode2 = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode2',level='Mode')
RALLmode2 = RALLmode2.unstack()
r21 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode2 - All")
r21.triangle( CFALLmode2['lung'] , RALLmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r22 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode2 - Liver")
r22.square( CFmode2['liver'] , Rmode2['liver'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r23 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode2 - Lung")
r23.circle( CFmode2['lung'] , Rmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
# ----------
CFmode3 = DataMatrix.loc[('Qm'),('CF')].xs('mode3',level='Mode')
CFmode3 = CFmode3.unstack()
CFALLmode3 = DataMatrix.loc[('Qm'),('CFALL')].xs('mode3',level='Mode')
CFALLmode3 = CFALLmode3.unstack()
Rmode3 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode3',level='Mode')
Rmode3 = Rmode3.unstack()
RALLmode3 = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode3',level='Mode')
RALLmode3 = RALLmode3.unstack()
r31 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode3 - All")
r31.triangle( CFALLmode3['lung'] , RALLmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r32 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode3 - Liver")
r32.square( CFmode3['liver'] , Rmode3['liver'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r33 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode3 - Lung")
r33.circle( CFmode3['lung'] , Rmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
# ----------
CFmode4 = DataMatrix.loc[('Qm'),('CF')].xs('mode4',level='Mode')
CFmode4 = CFmode4.unstack()
CFALLmode4 = DataMatrix.loc[('Qm'),('CFALL')].xs('mode4',level='Mode')
CFALLmode4 = CFALLmode4.unstack()
Rmode4 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode4',level='Mode')
Rmode4 = Rmode4.unstack()
RALLmode4 = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode4',level='Mode')
RALLmode4 = RALLmode4.unstack()
r41 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode4 - All")
r41.triangle( CFALLmode4['lung'] , RALLmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r42 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode4 - Liver")
r42.square( CFmode4['liver'] , Rmode4['liver'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r43 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode4 - Lung")
r43.circle( CFmode4['lung'] , Rmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
# ----------
CFmode5 = DataMatrix.loc[('Qm'),('CF')].xs('mode5',level='Mode')
CFmode5 = CFmode5.unstack()
CFALLmode5 = DataMatrix.loc[('Qm'),('CFALL')].xs('mode5',level='Mode')
CFALLmode5 = CFALLmode5.unstack()
Rmode5 = DataMatrix.loc[('Qm'),('Rsqrt')].xs('mode5',level='Mode')
Rmode5 = Rmode5.unstack()
RALLmode5 = DataMatrix.loc[('Qm'),('RsqrtALL')].xs('mode5',level='Mode')
RALLmode5 = RALLmode5.unstack()
r51 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode5 - All")
r51.triangle( CFALLmode5['lung'] , RALLmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r52 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode5 - Liver")
r52.square( CFmode5['liver'] , Rmode5['liver'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r53 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode5 - Lung")
r53.circle( CFmode5['lung'] , Rmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
p = gridplot([[r11, r21, r31, r41, r51], #, r21, r31, r41, r51],
[r12, r22, r32, r42, r52], #, r22, r32, r42, r52],
[r13, r23, r33, r43, r53]]) #, r23, r33, r43, r53],
show(p)
export_png(p, filename="plots/kp1000/CFR2QmMulti.png")
Out[41]:
Qs Plots - R2, CF, R2 vs CF:¶
\begin{equation} \normalsize \%idg \approx \frac{C_{tot}}{C_{out}}\ =\ \Bigg\{{\color{blue}{\mathcal{K}_{p}}} + \frac{K_{EC}\ \mathcal{L}_{EC,d}\ {\color{blue}{\varphi_{EC}}}}{D_{EC}} {\color{blue}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{EC,d}} + \Bigg\{\frac{K_{M}\ \mathcal{L}_{M,d}\ {\color{blue}{\varphi_{M}}}}{D_{M}} {\color{blue}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{M,d}} + \Bigg\{\frac{K_{EC}\ \mathcal{L}_{EC,d}\ {\color{red}{\varphi_{S}}}}{D_{EC}} {\color{blue}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{EC,d}} \end{equation}
Qec DataMatrix Statistics:¶
In [42]:
DataMatrix.loc[('Qs')].describe()[(['idg','normIDG','Rsqrt','RsqrtALL','CF','CFALL'])]
Out[42]:
In [43]:
Linemode1 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1
Out[43]:
Qs vs R2 Single plot:¶
In [44]:
# Plotting Qm vs R2 all in one plot
p = figure(title="Qs vs R2 ", plot_width=900)
#------------
Linemode1 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = p.line(QS, Linemode1all['lung'], line_width=3, line_color="tomato", muted_color="tomato", muted_alpha=0.1)
r12 = p.line(QS, Linemode1['liver'], line_width=3, line_color="purple", muted_color="purple", muted_alpha=0.1)
r13 = p.line(QS, Linemode1['lung'], line_width=3, line_color="blue", muted_color="blue", muted_alpha=0.1)
# r14 = p.line(COUT, Linemode1['heart'], line_width=3, line_color="green", muted_color="green", muted_alpha=0.1)
#------------
Linemode2 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = p.line(QS, Linemode2all['lung'], line_width=3, line_color="tomato", line_dash="dashed", muted_color="tomato", muted_alpha=0.1)
r22 = p.line(QS, Linemode2['liver'], line_width=3, line_color="purple", line_dash="dashed", muted_color="purple", muted_alpha=0.1)
r23 = p.line(QS, Linemode2['lung'], line_width=3, line_color="blue", line_dash="dashed", muted_color="blue", muted_alpha=0.1)
# r24 = p.line(COUT, Linemode2['heart'], line_width=3, line_color="green", line_dash="dashed", muted_color="green", muted_alpha=0.1)
#------------
Linemode3 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = p.line(QS, Linemode3all['lung'], line_width=3, line_color="tomato", line_dash="dotted", muted_color="tomato", muted_alpha=0.1)
r32 = p.line(QS, Linemode3['liver'], line_width=3, line_color="purple", line_dash="dotted", muted_color="purple", muted_alpha=0.1)
r33 = p.line(QS, Linemode3['lung'], line_width=3, line_color="blue", line_dash="dotted", muted_color="blue", muted_alpha=0.1)
# r34 = p.line(COUT, Linemode3['heart'], line_width=3, line_color="green", line_dash="dotted", muted_color="green", muted_alpha=0.1)
#------------
Linemode4 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = p.line(QS, Linemode4all['lung'], line_width=3, line_color="tomato", line_dash="dotdash", muted_color="tomato", muted_alpha=0.1)
r42 = p.line(QS, Linemode4['liver'], line_width=3, line_color="purple", line_dash="dotdash", muted_color="purple", muted_alpha=0.1)
r43 = p.line(QS, Linemode4['lung'], line_width=3, line_color="blue", line_dash="dotdash", muted_color="blue", muted_alpha=0.1)
# r44 = p.line(COUT, Linemode4['heart'], line_width=3, line_color="green", line_dash="dotdash", muted_color="green", muted_alpha=0.1)
#------------
Linemode5 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = p.line(QS, Linemode5all['lung'], line_width=3, line_color="tomato", line_dash="dashdot", muted_color="tomato", muted_alpha=0.1)
r52 = p.line(QS, Linemode5['liver'], line_width=3, line_color="purple", line_dash="dashdot", muted_color="purple", muted_alpha=0.1)
r53 = p.line(QS, Linemode5['lung'], line_width=3, line_color="blue", line_dash="dashdot", muted_color="blue", muted_alpha=0.1)
# r54 = p.line(COUT, Linemode5['heart'], line_width=3, line_color="green", line_dash="dashdot", muted_color="green", muted_alpha=0.1)
#------------
legend = Legend(items = [
("Mode1 - All Organs", [r11]),
("Mode1 - Liver", [r12]),
("Mode1 - Lung", [r13]),
#("Mode1 - Heart", [r14]),
("Mode2 - All Organs", [r21]),
("Mode2 - Liver", [r22]),
("Mode2 - Lung", [r23]),
#("Mode2 - Heart", [r24]),
("Mode3 - All Organs", [r31]),
("Mode3 - Liver", [r32]),
("Mode3 - Lung", [r33]),
#("Mode3 - Heart", [r34]),
("Mode4 - All Organs", [r41]),
("Mode4 - Liver", [r42]),
("Mode4 - Lung", [r43]),
#("Mode4 - Heart", [r44]),
("Mode5 - All Organs", [r51]),
("Mode5 - Liver", [r52]),
("Mode5 - Lung", [r53])], location = (0, -30))
#("Mode5 - Heart", [r54])], location = (0, -30))
p.add_layout(legend, "right")
p.legend.click_policy="mute"
p.xaxis.axis_label = 'Qs Range'
p.yaxis.axis_label = 'R2'
show(p)
export_png(p, filename="plots/kp1000/R2QsSingle.png")
Out[44]:
Qs vs R2 Multi plot:¶
In [45]:
# Plotting Qs vs R2 in seperate plots:
#------------
Linemode1 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = figure( y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - All")
r11.line(QS, Linemode1all['lung'], line_width=3, line_color="tomato")
r12 = figure( y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Liver")
r12.line(QS, Linemode1['liver'], line_width=3, line_color="purple")
# r12.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r13 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Lung")
r13.line(QS, Linemode1['lung'], line_width=3, line_color="blue")
# r14 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Heart")
# r14.line(COUT, Linemode1['heart'], line_width=3, line_color="green")
# #------------
Linemode2 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - All")
r21.line(QS, Linemode2all['lung'], line_width=3, line_color="tomato")
r22 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Liver")
r22.line(QS, Linemode2['liver'], line_width=3, line_color="purple")
# r22.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r23 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Lung")
r23.line(QS, Linemode2['lung'], line_width=3, line_color="blue")
# r24 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Heart")
# r24.line(COUT, Linemode2['heart'], line_width=3, line_color="green")
# #------------
Linemode3 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - All")
r31.line(QS, Linemode3all['lung'], line_width=3, line_color="tomato")
r32 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Liver")
r32.line(QS, Linemode3['liver'], line_width=3, line_color="purple")
# r32.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r33 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Lung")
r33.line(QS, Linemode3['lung'], line_width=3, line_color="blue")
# r34 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Heart")
# r34.line(COUT, Linemode3['heart'], line_width=3, line_color="green")
# #------------
Linemode4 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - All")
r41.line(QS, Linemode4all['lung'], line_width=3, line_color="tomato")
r42 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Liver")
r42.line(QS, Linemode4['liver'], line_width=3, line_color="purple")
# r42.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r43 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Lung")
r43.line(QS, Linemode4['lung'], line_width=3, line_color="blue")
# r44 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Heart")
# r44.line(COUT, Linemode4['heart'], line_width=3, line_color="green")
# #------------
Linemode5 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - All")
r51.line(QS, Linemode5all['lung'], line_width=3, line_color="tomato")
r52 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Liver")
r52.line(QS, Linemode5['liver'], line_width=3, line_color="purple")
# r52.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r53 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Lung")
r53.line(QS, Linemode5['lung'], line_width=3, line_color="blue")
# r54 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Heart")
# r54.line(COUT, Linemode5['heart'], line_width=3, line_color="green")
# #------------
p = gridplot([[r11, r21, r31, r41, r51],
[r12, r22, r32, r42, r52],
[r13, r23, r33, r43, r53]])
show(p)
export_png(p, filename="plots/kp1000/R2QsMulti.png")
Out[45]:
Qs vs CF Single plot:¶
In [46]:
# Plotting Qs vs R2 all in one plot
p = figure(title="Qs vs CF", plot_width=900, y_range= (0,1))
#------------
Linemode1 = DataMatrix.loc[('Qs'),('CF')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Qs'),('CFALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = p.line(QS, Linemode1all['lung'], line_width=3, line_color="tomato", muted_color="tomato", muted_alpha=0.1)
r12 = p.line(QS, Linemode1['liver'], line_width=3, line_color="purple", muted_color="purple", muted_alpha=0.1)
r13 = p.line(QS, Linemode1['lung'], line_width=3, line_color="blue", muted_color="blue", muted_alpha=0.1)
# r14 = p.line(COUT, Linemode1['heart'], line_width=3, line_color="green", muted_color="green", muted_alpha=0.1)
#------------
Linemode2 = DataMatrix.loc[('Qs'),('CF')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Qs'),('CFALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = p.line(QS, Linemode2all['lung'], line_width=3, line_color="tomato", line_dash="dashed", muted_color="tomato", muted_alpha=0.1)
r22 = p.line(QS, Linemode2['liver'], line_width=3, line_color="purple", line_dash="dashed", muted_color="purple", muted_alpha=0.1)
r23 = p.line(QS, Linemode2['lung'], line_width=3, line_color="blue", line_dash="dashed", muted_color="blue", muted_alpha=0.1)
# r24 = p.line(QEC, Linemode2['heart'], line_width=3, line_color="green", line_dash="dashed", muted_color="green", muted_alpha=0.1)
#------------
Linemode3 = DataMatrix.loc[('Qs'),('CF')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Qs'),('CFALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = p.line(QS, Linemode3all['lung'], line_width=3, line_color="tomato", line_dash="dotted", muted_color="tomato", muted_alpha=0.1)
r32 = p.line(QS, Linemode3['liver'], line_width=3, line_color="purple", line_dash="dotted", muted_color="purple", muted_alpha=0.1)
r33 = p.line(QS, Linemode3['lung'], line_width=3, line_color="blue", line_dash="dotted", muted_color="blue", muted_alpha=0.1)
# r34 = p.line(QEC, Linemode3['heart'], line_width=3, line_color="green", line_dash="dotted", muted_color="green", muted_alpha=0.1)
#------------
Linemode4 = DataMatrix.loc[('Qs'),('CF')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Qs'),('CFALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = p.line(QS, Linemode4all['lung'], line_width=3, line_color="tomato", line_dash="dotdash", muted_color="tomato", muted_alpha=0.1)
r42 = p.line(QS, Linemode4['liver'], line_width=3, line_color="purple", line_dash="dotdash", muted_color="purple", muted_alpha=0.1)
r43 = p.line(QS, Linemode4['lung'], line_width=3, line_color="blue", line_dash="dotdash", muted_color="blue", muted_alpha=0.1)
# r44 = p.line(QEC, Linemode4['heart'], line_width=3, line_color="green", line_dash="dotdash", muted_color="green", muted_alpha=0.1)
#------------
Linemode5 = DataMatrix.loc[('Qs'),('CF')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Qs'),('CFALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = p.line(QS, Linemode5all['lung'], line_width=3, line_color="tomato", line_dash="dashdot", muted_color="tomato", muted_alpha=0.1)
r52 = p.line(QS, Linemode5['liver'], line_width=3, line_color="purple", line_dash="dashdot", muted_color="purple", muted_alpha=0.1)
r53 = p.line(QS, Linemode5['lung'], line_width=3, line_color="blue", line_dash="dashdot", muted_color="blue", muted_alpha=0.1)
# r54 = p.line(QEC, Linemode5['heart'], line_width=3, line_color="green", line_dash="dashdot", muted_color="green", muted_alpha=0.1)
#------------
# from bokeh.models import Legend
legend = Legend(items = [
("Mode1 - All Organs", [r11]),
("Mode1 - Liver", [r12]),
("Mode1 - Lung", [r13]),
# ("Mode1 - Heart", [r14]),
("Mode2 - All Organs", [r21]),
("Mode2 - Liver", [r22]),
("Mode2 - Lung", [r23]),
# ("Mode2 - Heart", [r24]),
("Mode3 - All Organs", [r31]),
("Mode3 - Liver", [r32]),
("Mode3 - Lung", [r33]),
# ("Mode3 - Heart", [r34]),
("Mode4 - All Organs", [r41]),
("Mode4 - Liver", [r42]),
("Mode4 - Lung", [r43]),
# ("Mode4 - Heart", [r44]),
("Mode5 - All Organs", [r51]),
("Mode5 - Liver", [r52]),
("Mode5 - Lung", [r53])], location = (0, -30))
# ("Mode5 - Heart", [r54])
p.add_layout(legend, "right")
p.legend.click_policy="mute"
p.xaxis.axis_label = 'Qs Range'
p.yaxis.axis_label = 'CF'
show(p)
export_png(p, filename="plots/kp1000/CFQsSingle.png")
Out[46]:
Qs vs CF Multi plot:¶
In [47]:
# Plotting Qs vs R2 in seperate plots:
#------------
Linemode1 = DataMatrix.loc[('Qs'),('CF')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Qs'),('CFALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - All")
r11.line(QS, Linemode1all['lung'], line_width=3, line_color="tomato")
r12 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Liver")
r12.line(QS, Linemode1['liver'], line_width=3, line_color="purple")
# r12.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r13 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Lung")
r13.line(QS, Linemode1['lung'], line_width=3, line_color="blue")
# r14 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode1 - Heart")
# r14.line(COUT, Linemode1['heart'], line_width=3, line_color="green")
# #------------
Linemode2 = DataMatrix.loc[('Qs'),('CF')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Qs'),('CFALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = figure( y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - All")
r21.line(QS, Linemode2all['lung'], line_width=3, line_color="tomato")
r22 = figure(y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode2 - Liver")
r22.line(QS, Linemode2['liver'], line_width=3, line_color="purple")
# r22.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r23 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Lung")
r23.line(QS, Linemode2['lung'], line_width=3, line_color="blue")
# r24 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode2 - Heart")
# r24.line(COUT, Linemode2['heart'], line_width=3, line_color="green")
# #------------
Linemode3 = DataMatrix.loc[('Qs'),('CF')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Qs'),('CFALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - All")
r31.line(QS, Linemode3all['lung'], line_width=3, line_color="tomato")
r32 = figure(y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode3 - Liver")
r32.line(QS, Linemode3['liver'], line_width=3, line_color="purple")
# r32.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r33 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Lung")
r33.line(QS, Linemode3['lung'], line_width=3, line_color="blue")
# r34 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode3 - Heart")
# r34.line(COUT, Linemode3['heart'], line_width=3, line_color="green")
# #------------
Linemode4 = DataMatrix.loc[('Qs'),('CF')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Qs'),('CFALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - All")
r41.line(QS, Linemode4all['lung'], line_width=3, line_color="tomato")
r42 = figure(y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode4 - Liver")
r42.line(QS, Linemode4['liver'], line_width=3, line_color="purple")
# r42.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r43 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Lung")
r43.line(QS, Linemode4['lung'], line_width=3, line_color="blue")
# r44 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode4 - Heart")
# r44.line(COUT, Linemode4['heart'], line_width=3, line_color="green")
# #------------
Linemode5 = DataMatrix.loc[('Qs'),('CF')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Qs'),('CFALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - All")
r51.line(QS, Linemode5all['lung'], line_width=3, line_color="tomato")
r52 = figure(y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode5 - Liver")
r52.line(QS, Linemode5['liver'], line_width=3, line_color="purple")
# r52.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r53 = figure(y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Lung")
r53.line(QS, Linemode5['lung'], line_width=3, line_color="blue")
# r54 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode5 - Heart")
# r54.line(COUT, Linemode5['heart'], line_width=3, line_color="green")
# #------------
p = gridplot([[r11, r21, r31, r41, r51],
[r12, r22, r32, r42, r52],
[r13, r23, r33, r43, r53]])
show(p)
export_png(p, filename="plots/kp1000/CFQsMulti.png")
Out[47]:
R2 vs CF for Qs single plot:¶
In [48]:
# Plotting R2 vs CF for Qs all in one plot
p = figure(title="R2 vs CF ", plot_width=900, x_range = (0, 1), y_range = (0, 1)) #
#------------
CFmode1 = DataMatrix.loc[('Qs'),('CF')].xs('mode1',level='Mode')
CFmode1 = CFmode1.unstack()
CFALLmode1 = DataMatrix.loc[('Qs'),('CFALL')].xs('mode1',level='Mode')
CFALLmode1 = CFALLmode1.unstack()
Rmode1 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode1',level='Mode')
Rmode1 = Rmode1.unstack()
RALLmode1 = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode1',level='Mode')
RALLmode1 = RALLmode1.unstack()
r11 = p.triangle( CFALLmode1['lung'] , RALLmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r12 = p.square( CFmode1['liver'] , Rmode1['liver'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r13 = p.circle( CFmode1['lung'] , Rmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
#------------
CFmode2 = DataMatrix.loc[('Qs'),('CF')].xs('mode2',level='Mode')
CFmode2 = CFmode2.unstack()
CFALLmode2 = DataMatrix.loc[('Qs'),('CFALL')].xs('mode2',level='Mode')
CFALLmode2 = CFALLmode2.unstack()
Rmode2 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode2',level='Mode')
Rmode2 = Rmode2.unstack()
RALLmode2 = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode2',level='Mode')
RALLmode2 = RALLmode2.unstack()
r21 = p.triangle( CFALLmode2['lung'] , RALLmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r22 = p.square( CFmode2['liver'] , Rmode2['liver'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r23 = p.circle( CFmode2['lung'] , Rmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
#------------
CFmode3 = DataMatrix.loc[('Qs'),('CF')].xs('mode3',level='Mode')
CFmode3 = CFmode3.unstack()
CFALLmode3 = DataMatrix.loc[('Qs'),('CFALL')].xs('mode3',level='Mode')
CFALLmode3 = CFALLmode3.unstack()
Rmode3 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode3',level='Mode')
Rmode3 = Rmode3.unstack()
RALLmode3 = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode3',level='Mode')
RALLmode3 = RALLmode3.unstack()
r31 = p.triangle( CFALLmode3['lung'] , RALLmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r32 = p.square( CFmode3['liver'] , Rmode3['liver'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r33 = p.circle( CFmode3['lung'] , Rmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
#------------
CFmode4 = DataMatrix.loc[('Qs'),('CF')].xs('mode4',level='Mode')
CFmode4 = CFmode4.unstack()
CFALLmode4 = DataMatrix.loc[('Qs'),('CFALL')].xs('mode4',level='Mode')
CFALLmode4 = CFALLmode4.unstack()
Rmode4 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode4',level='Mode')
Rmode4 = Rmode4.unstack()
RALLmode4 = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode4',level='Mode')
RALLmode4 = RALLmode4.unstack()
r41 = p.triangle( CFALLmode4['lung'] , RALLmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r42 = p.square( CFmode4['liver'] , Rmode4['liver'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r43 = p.circle( CFmode4['lung'] , Rmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
#------------
CFmode5 = DataMatrix.loc[('Qs'),('CF')].xs('mode5',level='Mode')
CFmode5 = CFmode5.unstack()
CFALLmode5 = DataMatrix.loc[('Qs'),('CFALL')].xs('mode5',level='Mode')
CFALLmode5 = CFALLmode5.unstack()
Rmode5 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode5',level='Mode')
Rmode5 = Rmode5.unstack()
RALLmode5 = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode5',level='Mode')
RALLmode5 = RALLmode5.unstack()
r51 = p.triangle( CFALLmode5['lung'] , RALLmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r52 = p.square( CFmode5['liver'] , Rmode5['liver'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r53 = p.circle( CFmode5['lung'] , Rmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
legend = Legend(items = [
("Mode1 - All Organs", [r11]),
("Mode1 - Liver", [r12]),
("Mode1 - Lung", [r13]),
("Mode2 - All Organs", [r21]),
("Mode2 - Liver", [r22]),
("Mode2 - Lung", [r23]),
("Mode3 - All Organs", [r31]),
("Mode3 - Liver", [r32]),
("Mode3 - Lung", [r33]),
("Mode4 - All Organs", [r41]),
("Mode4 - Liver", [r42]),
("Mode4 - Lung", [r43]),
("Mode5 - All Organs", [r51]),
("Mode5 - Liver", [r52]),
("Mode5 - Lung", [r53])], location = (0, -30))
p.add_layout(legend, "right")
p.legend.click_policy="mute"
p.xaxis.axis_label = 'CF'
p.yaxis.axis_label = 'R2'
show(p)
export_png(p, filename="plots/kp1000/CFR2QsSingle.png")
Out[48]:
R2 vs CF for Qs Miltiple plot:¶
In [49]:
# Plotting Qs vs R2 in seperate plots:
#------------
p = figure(title="R2 vs CF ", plot_width=900, x_range = (0, 1), y_range = (0, 1)) #
#------------
CFmode1 = DataMatrix.loc[('Qs'),('CF')].xs('mode1',level='Mode')
CFmode1 = CFmode1.unstack()
CFALLmode1 = DataMatrix.loc[('Qs'),('CFALL')].xs('mode1',level='Mode')
CFALLmode1 = CFALLmode1.unstack()
Rmode1 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode1',level='Mode')
Rmode1 = Rmode1.unstack()
RALLmode1 = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode1',level='Mode')
RALLmode1 = RALLmode1.unstack()
r11 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode1 - All")
r11.triangle( CFALLmode1['lung'] , RALLmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r12 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode1 - Liver")
r12.square( CFmode1['liver'] , Rmode1['liver'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r13 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode1 - Lung")
r13.circle( CFmode1['lung'] , Rmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
# ----------
CFmode2 = DataMatrix.loc[('Qs'),('CF')].xs('mode2',level='Mode')
CFmode2 = CFmode2.unstack()
CFALLmode2 = DataMatrix.loc[('Qs'),('CFALL')].xs('mode2',level='Mode')
CFALLmode2 = CFALLmode2.unstack()
Rmode2 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode2',level='Mode')
Rmode2 = Rmode2.unstack()
RALLmode2 = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode2',level='Mode')
RALLmode2 = RALLmode2.unstack()
r21 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode2 - All")
r21.triangle( CFALLmode2['lung'] , RALLmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r22 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode2 - Liver")
r22.square( CFmode2['liver'] , Rmode2['liver'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r23 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode2 - Lung")
r23.circle( CFmode2['lung'] , Rmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
# ----------
CFmode3 = DataMatrix.loc[('Qs'),('CF')].xs('mode3',level='Mode')
CFmode3 = CFmode3.unstack()
CFALLmode3 = DataMatrix.loc[('Qs'),('CFALL')].xs('mode3',level='Mode')
CFALLmode3 = CFALLmode3.unstack()
Rmode3 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode3',level='Mode')
Rmode3 = Rmode3.unstack()
RALLmode3 = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode3',level='Mode')
RALLmode3 = RALLmode3.unstack()
r31 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode3 - All")
r31.triangle( CFALLmode3['lung'] , RALLmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r32 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode3 - Liver")
r32.square( CFmode3['liver'] , Rmode3['liver'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r33 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode3 - Lung")
r33.circle( CFmode3['lung'] , Rmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
# ----------
CFmode4 = DataMatrix.loc[('Qs'),('CF')].xs('mode4',level='Mode')
CFmode4 = CFmode4.unstack()
CFALLmode4 = DataMatrix.loc[('Qs'),('CFALL')].xs('mode4',level='Mode')
CFALLmode4 = CFALLmode4.unstack()
Rmode4 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode4',level='Mode')
Rmode4 = Rmode4.unstack()
RALLmode4 = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode4',level='Mode')
RALLmode4 = RALLmode4.unstack()
r41 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode4 - All")
r41.triangle( CFALLmode4['lung'] , RALLmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r42 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode4 - Liver")
r42.square( CFmode4['liver'] , Rmode4['liver'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r43 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode4 - Lung")
r43.circle( CFmode4['lung'] , Rmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
# ----------
CFmode5 = DataMatrix.loc[('Qs'),('CF')].xs('mode5',level='Mode')
CFmode5 = CFmode5.unstack()
CFALLmode5 = DataMatrix.loc[('Qs'),('CFALL')].xs('mode5',level='Mode')
CFALLmode5 = CFALLmode5.unstack()
Rmode5 = DataMatrix.loc[('Qs'),('Rsqrt')].xs('mode5',level='Mode')
Rmode5 = Rmode5.unstack()
RALLmode5 = DataMatrix.loc[('Qs'),('RsqrtALL')].xs('mode5',level='Mode')
RALLmode5 = RALLmode5.unstack()
r51 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode5 - All")
r51.triangle( CFALLmode5['lung'] , RALLmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r52 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode5 - Liver")
r52.square( CFmode5['liver'] , Rmode5['liver'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r53 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode5 - Lung")
r53.circle( CFmode5['lung'] , Rmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
p = gridplot([[r11, r21, r31, r41, r51], #, r21, r31, r41, r51],
[r12, r22, r32, r42, r52], #, r22, r32, r42, r52],
[r13, r23, r33, r43, r53]]) #, r23, r33, r43, r53],
show(p)
export_png(p, filename="plots/kp1000/CFR2QsMULTI.png")
Out[49]:
Kp Plots - R2, CF, R2 vs CF:¶
\begin{equation} \normalsize \%idg \approx \frac{C_{tot}}{C_{out}}\ =\ \Bigg\{{\color{red}{\mathcal{K}_{p}}} + \frac{K_{EC}\ \mathcal{L}_{EC,d}\ {\color{blue}{\varphi_{EC}}}}{D_{EC}} {\color{blue}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{EC,d}} + \Bigg\{\frac{K_{M}\ \mathcal{L}_{M,d}\ {\color{blue}{\varphi_{M}}}}{D_{M}} {\color{blue}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{M,d}} + \Bigg\{\frac{K_{EC}\ \mathcal{L}_{EC,d}\ {\color{blue}{\varphi_{S}}}}{D_{EC}} {\color{blue}{C_{out}}}\ \Bigg\} \frac{L_{cap}}{\mathcal{L}_{EC,d}} \end{equation}
Kp DataMatrix Statistics:¶
In [50]:
DataMatrix.loc[('Kp')].describe()[(['idg','normIDG','Rsqrt','RsqrtALL','CF','CFALL'])]
Out[50]:
In [51]:
Linemode1 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1
Out[51]:
In [52]:
KP
Out[52]:
Kp vs R2 Single plot:¶
In [53]:
# Plotting Kp vs R2 all in one plot
p = figure(title="Kp vs R2 ", plot_width=900,x_axis_type="log")
#------------
Linemode1 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = p.line(KP, Linemode1all['lung'], line_width=3, line_color="tomato", muted_color="tomato", muted_alpha=0.1)
r12 = p.line(KP, Linemode1['liver'], line_width=3, line_color="purple", muted_color="purple", muted_alpha=0.1)
r13 = p.line(KP, Linemode1['lung'], line_width=3, line_color="blue", muted_color="blue", muted_alpha=0.1)
# r14 = p.line(COUT, Linemode1['heart'], line_width=3, line_color="green", muted_color="green", muted_alpha=0.1)
#------------
Linemode2 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = p.line(KP, Linemode2all['lung'], line_width=3, line_color="tomato", line_dash="dashed", muted_color="tomato", muted_alpha=0.1)
r22 = p.line(KP, Linemode2['liver'], line_width=3, line_color="purple", line_dash="dashed", muted_color="purple", muted_alpha=0.1)
r23 = p.line(KP, Linemode2['lung'], line_width=3, line_color="blue", line_dash="dashed", muted_color="blue", muted_alpha=0.1)
# r24 = p.line(COUT, Linemode2['heart'], line_width=3, line_color="green", line_dash="dashed", muted_color="green", muted_alpha=0.1)
#------------
Linemode3 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = p.line(KP, Linemode3all['lung'], line_width=3, line_color="tomato", line_dash="dotted", muted_color="tomato", muted_alpha=0.1)
r32 = p.line(KP, Linemode3['liver'], line_width=3, line_color="purple", line_dash="dotted", muted_color="purple", muted_alpha=0.1)
r33 = p.line(KP, Linemode3['lung'], line_width=3, line_color="blue", line_dash="dotted", muted_color="blue", muted_alpha=0.1)
# r34 = p.line(COUT, Linemode3['heart'], line_width=3, line_color="green", line_dash="dotted", muted_color="green", muted_alpha=0.1)
#------------
Linemode4 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = p.line(KP, Linemode4all['lung'], line_width=3, line_color="tomato", line_dash="dotdash", muted_color="tomato", muted_alpha=0.1)
r42 = p.line(KP, Linemode4['liver'], line_width=3, line_color="purple", line_dash="dotdash", muted_color="purple", muted_alpha=0.1)
r43 = p.line(KP, Linemode4['lung'], line_width=3, line_color="blue", line_dash="dotdash", muted_color="blue", muted_alpha=0.1)
# r44 = p.line(COUT, Linemode4['heart'], line_width=3, line_color="green", line_dash="dotdash", muted_color="green", muted_alpha=0.1)
#------------
Linemode5 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = p.line(KP, Linemode5all['lung'], line_width=3, line_color="tomato", line_dash="dashdot", muted_color="tomato", muted_alpha=0.1)
r52 = p.line(KP, Linemode5['liver'], line_width=3, line_color="purple", line_dash="dashdot", muted_color="purple", muted_alpha=0.1)
r53 = p.line(KP, Linemode5['lung'], line_width=3, line_color="blue", line_dash="dashdot", muted_color="blue", muted_alpha=0.1)
# r54 = p.line(COUT, Linemode5['heart'], line_width=3, line_color="green", line_dash="dashdot", muted_color="green", muted_alpha=0.1)
#------------
legend = Legend(items = [
("Mode1 - All Organs", [r11]),
("Mode1 - Liver", [r12]),
("Mode1 - Lung", [r13]),
#("Mode1 - Heart", [r14]),
("Mode2 - All Organs", [r21]),
("Mode2 - Liver", [r22]),
("Mode2 - Lung", [r23]),
#("Mode2 - Heart", [r24]),
("Mode3 - All Organs", [r31]),
("Mode3 - Liver", [r32]),
("Mode3 - Lung", [r33]),
#("Mode3 - Heart", [r34]),
("Mode4 - All Organs", [r41]),
("Mode4 - Liver", [r42]),
("Mode4 - Lung", [r43]),
#("Mode4 - Heart", [r44]),
("Mode5 - All Organs", [r51]),
("Mode5 - Liver", [r52]),
("Mode5 - Lung", [r53])], location = (0, -30))
#("Mode5 - Heart", [r54])], location = (0, -30))
p.add_layout(legend, "right")
p.legend.click_policy="mute"
p.xaxis.axis_label = 'Kp Range'
p.yaxis.axis_label = 'R2'
show(p)
export_png(p, filename="KpR2Single.png")
Out[53]:
Kp vs R2 Multi plot:¶
In [54]:
# Plotting Qs vs R2 in seperate plots:
#------------
Linemode1 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - All")
r11.line(KP, Linemode1all['lung'], line_width=3, line_color="tomato")
r12 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Liver")
r12.line(KP, Linemode1['liver'], line_width=3, line_color="purple")
# r12.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r13 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Lung")
r13.line(KP, Linemode1['lung'], line_width=3, line_color="blue")
# r14 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Heart")
# r14.line(COUT, Linemode1['heart'], line_width=3, line_color="green")
# #------------
Linemode2 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - All")
r21.line(KP, Linemode2all['lung'], line_width=3, line_color="tomato")
r22 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Liver")
r22.line(KP, Linemode2['liver'], line_width=3, line_color="purple")
# r22.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r23 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Lung")
r23.line(KP, Linemode2['lung'], line_width=3, line_color="blue")
# r24 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Heart")
# r24.line(COUT, Linemode2['heart'], line_width=3, line_color="green")
# #------------
Linemode3 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - All")
r31.line(KP, Linemode3all['lung'], line_width=3, line_color="tomato")
r32 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Liver")
r32.line(KP, Linemode3['liver'], line_width=3, line_color="purple")
# r32.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r33 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Lung")
r33.line(KP, Linemode3['lung'], line_width=3, line_color="blue")
# r34 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Heart")
# r34.line(COUT, Linemode3['heart'], line_width=3, line_color="green")
# #------------
Linemode4 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - All")
r41.line(KP, Linemode4all['lung'], line_width=3, line_color="tomato")
r42 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Liver")
r42.line(KP, Linemode4['liver'], line_width=3, line_color="purple")
# r42.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r43 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Lung")
r43.line(KP, Linemode4['lung'], line_width=3, line_color="blue")
# r44 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Heart")
# r44.line(COUT, Linemode4['heart'], line_width=3, line_color="green")
# #------------
Linemode5 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - All")
r51.line(KP, Linemode5all['lung'], line_width=3, line_color="tomato")
r52 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Liver")
r52.line(KP, Linemode5['liver'], line_width=3, line_color="purple")
# r52.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r53 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Lung")
r53.line(KP, Linemode5['lung'], line_width=3, line_color="blue")
# r54 = figure(x_axis_type="log", y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Heart")
# r54.line(COUT, Linemode5['heart'], line_width=3, line_color="green")
# #------------
p = gridplot([[r11, r21, r31, r41, r51],
[r12, r22, r32, r42, r52],
[r13, r23, r33, r43, r53]])
show(p)
export_png(p, filename="KpR2Multi.png")
Out[54]:
Kp vs CF Single plot:¶
In [55]:
# Plotting Kp vs R2 all in one plot
p = figure(title="Kp vs CF", plot_width=900, y_range= (0,1), x_axis_type="log",)
#------------
Linemode1 = DataMatrix.loc[('Kp'),('CF')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Kp'),('CFALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = p.line(KP, Linemode1all['lung'], line_width=3, line_color="tomato", muted_color="tomato", muted_alpha=0.1)
r12 = p.line(KP, Linemode1['liver'], line_width=3, line_color="purple", muted_color="purple", muted_alpha=0.1)
r13 = p.line(KP, Linemode1['lung'], line_width=3, line_color="blue", muted_color="blue", muted_alpha=0.1)
# r14 = p.line(COUT, Linemode1['heart'], line_width=3, line_color="green", muted_color="green", muted_alpha=0.1)
#------------
Linemode2 = DataMatrix.loc[('Kp'),('CF')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Kp'),('CFALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = p.line(KP, Linemode2all['lung'], line_width=3, line_color="tomato", line_dash="dashed", muted_color="tomato", muted_alpha=0.1)
r22 = p.line(KP, Linemode2['liver'], line_width=3, line_color="purple", line_dash="dashed", muted_color="purple", muted_alpha=0.1)
r23 = p.line(KP, Linemode2['lung'], line_width=3, line_color="blue", line_dash="dashed", muted_color="blue", muted_alpha=0.1)
# r24 = p.line(QEC, Linemode2['heart'], line_width=3, line_color="green", line_dash="dashed", muted_color="green", muted_alpha=0.1)
#------------
Linemode3 = DataMatrix.loc[('Kp'),('CF')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Kp'),('CFALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = p.line(KP, Linemode3all['lung'], line_width=3, line_color="tomato", line_dash="dotted", muted_color="tomato", muted_alpha=0.1)
r32 = p.line(KP, Linemode3['liver'], line_width=3, line_color="purple", line_dash="dotted", muted_color="purple", muted_alpha=0.1)
r33 = p.line(KP, Linemode3['lung'], line_width=3, line_color="blue", line_dash="dotted", muted_color="blue", muted_alpha=0.1)
# r34 = p.line(QEC, Linemode3['heart'], line_width=3, line_color="green", line_dash="dotted", muted_color="green", muted_alpha=0.1)
#------------
Linemode4 = DataMatrix.loc[('Kp'),('CF')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Kp'),('CFALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = p.line(KP, Linemode4all['lung'], line_width=3, line_color="tomato", line_dash="dotdash", muted_color="tomato", muted_alpha=0.1)
r42 = p.line(KP, Linemode4['liver'], line_width=3, line_color="purple", line_dash="dotdash", muted_color="purple", muted_alpha=0.1)
r43 = p.line(KP, Linemode4['lung'], line_width=3, line_color="blue", line_dash="dotdash", muted_color="blue", muted_alpha=0.1)
# r44 = p.line(QEC, Linemode4['heart'], line_width=3, line_color="green", line_dash="dotdash", muted_color="green", muted_alpha=0.1)
#------------
Linemode5 = DataMatrix.loc[('Kp'),('CF')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Kp'),('CFALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = p.line(KP, Linemode5all['lung'], line_width=3, line_color="tomato", line_dash="dashdot", muted_color="tomato", muted_alpha=0.1)
r52 = p.line(KP, Linemode5['liver'], line_width=3, line_color="purple", line_dash="dashdot", muted_color="purple", muted_alpha=0.1)
r53 = p.line(KP, Linemode5['lung'], line_width=3, line_color="blue", line_dash="dashdot", muted_color="blue", muted_alpha=0.1)
# r54 = p.line(QEC, Linemode5['heart'], line_width=3, line_color="green", line_dash="dashdot", muted_color="green", muted_alpha=0.1)
#------------
# from bokeh.models import Legend
legend = Legend(items = [
("Mode1 - All Organs", [r11]),
("Mode1 - Liver", [r12]),
("Mode1 - Lung", [r13]),
# ("Mode1 - Heart", [r14]),
("Mode2 - All Organs", [r21]),
("Mode2 - Liver", [r22]),
("Mode2 - Lung", [r23]),
# ("Mode2 - Heart", [r24]),
("Mode3 - All Organs", [r31]),
("Mode3 - Liver", [r32]),
("Mode3 - Lung", [r33]),
# ("Mode3 - Heart", [r34]),
("Mode4 - All Organs", [r41]),
("Mode4 - Liver", [r42]),
("Mode4 - Lung", [r43]),
# ("Mode4 - Heart", [r44]),
("Mode5 - All Organs", [r51]),
("Mode5 - Liver", [r52]),
("Mode5 - Lung", [r53])], location = (0, -30))
# ("Mode5 - Heart", [r54])
p.add_layout(legend, "right")
p.legend.click_policy="mute"
p.xaxis.axis_label = 'Kp Range'
p.yaxis.axis_label = 'CF'
show(p)
export_png(p, filename="KpCFSingle.png")
Out[55]:
Kp vs CF Multi plot:¶
In [56]:
# Plotting Qs vs R2 in seperate plots:
#------------
Linemode1 = DataMatrix.loc[('Kp'),('CF')].xs('mode1',level='Mode')
Linemode1 = Linemode1.unstack()
Linemode1all = DataMatrix.loc[('Kp'),('CFALL')].xs('mode1',level='Mode')
Linemode1all = Linemode1all.unstack()
r11 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - All")
r11.line(KP, Linemode1all['lung'], line_width=3, line_color="tomato")
r12 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Liver")
r12.line(KP, Linemode1['liver'], line_width=3, line_color="purple")
# r12.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r13 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode1 - Lung")
r13.line(KP, Linemode1['lung'], line_width=3, line_color="blue")
# r14 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode1 - Heart")
# r14.line(COUT, Linemode1['heart'], line_width=3, line_color="green")
# #------------
Linemode2 = DataMatrix.loc[('Kp'),('CF')].xs('mode2',level='Mode')
Linemode2 = Linemode2.unstack()
Linemode2all = DataMatrix.loc[('Kp'),('CFALL')].xs('mode2',level='Mode')
Linemode2all = Linemode2all.unstack()
r21 = figure( x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - All")
r21.line(KP, Linemode2all['lung'], line_width=3, line_color="tomato")
r22 = figure(x_axis_type="log",y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode2 - Liver")
r22.line(KP, Linemode2['liver'], line_width=3, line_color="purple")
# r22.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r23 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode2 - Lung")
r23.line(KP, Linemode2['lung'], line_width=3, line_color="blue")
# r24 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode2 - Heart")
# r24.line(COUT, Linemode2['heart'], line_width=3, line_color="green")
# #------------
Linemode3 = DataMatrix.loc[('Kp'),('CF')].xs('mode3',level='Mode')
Linemode3 = Linemode3.unstack()
Linemode3all = DataMatrix.loc[('Kp'),('CFALL')].xs('mode3',level='Mode')
Linemode3all = Linemode3all.unstack()
r31 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - All")
r31.line(KP, Linemode3all['lung'], line_width=3, line_color="tomato")
r32 = figure(x_axis_type="log",y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode3 - Liver")
r32.line(KP, Linemode3['liver'], line_width=3, line_color="purple")
# r32.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r33 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode3 - Lung")
r33.line(KP, Linemode3['lung'], line_width=3, line_color="blue")
# r34 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode3 - Heart")
# r34.line(COUT, Linemode3['heart'], line_width=3, line_color="green")
# #------------
Linemode4 = DataMatrix.loc[('Kp'),('CF')].xs('mode4',level='Mode')
Linemode4 = Linemode4.unstack()
Linemode4all = DataMatrix.loc[('Kp'),('CFALL')].xs('mode4',level='Mode')
Linemode4all = Linemode4all.unstack()
r41 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - All")
r41.line(KP, Linemode4all['lung'], line_width=3, line_color="tomato")
r42 = figure(x_axis_type="log",y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode4 - Liver")
r42.line(KP, Linemode4['liver'], line_width=3, line_color="purple")
# r42.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r43 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode4 - Lung")
r43.line(KP, Linemode4['lung'], line_width=3, line_color="blue")
# r44 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode4 - Heart")
# r44.line(COUT, Linemode4['heart'], line_width=3, line_color="green")
# #------------
Linemode5 = DataMatrix.loc[('Kp'),('CF')].xs('mode5',level='Mode')
Linemode5 = Linemode5.unstack()
Linemode5all = DataMatrix.loc[('Kp'),('CFALL')].xs('mode5',level='Mode')
Linemode5all = Linemode5all.unstack()
r51 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - All")
r51.line(KP, Linemode5all['lung'], line_width=3, line_color="tomato")
r52 = figure(x_axis_type="log",y_range = (-0.5,1), plot_width=300, plot_height=300, title="Mode5 - Liver")
r52.line(KP, Linemode5['liver'], line_width=3, line_color="purple")
# r52.line([1E-13,1E-9], [0,0], line_width=1, line_color ='black')
r53 = figure(x_axis_type="log",y_range = (0,1), plot_width=300, plot_height=300, title="Mode5 - Lung")
r53.line(KP, Linemode5['lung'], line_width=3, line_color="blue")
# r54 = figure(x_axis_type="log", y_range = (0,1.1), plot_width=300, plot_height=300, title="Mode5 - Heart")
# r54.line(COUT, Linemode5['heart'], line_width=3, line_color="green")
# #------------
p = gridplot([[r11, r21, r31, r41, r51],
[r12, r22, r32, r42, r52],
[r13, r23, r33, r43, r53]])
show(p)
export_png(p, filename="KpCFMulti.png")
Out[56]:
R2 vs CF for Kp single plot:¶
In [57]:
# Plotting R2 vs CF for Kp all in one plot
p = figure(title="R2 vs CF ", plot_width=900, x_range = (0, 1), y_range = (0, 1)) #
#------------
CFmode1 = DataMatrix.loc[('Kp'),('CF')].xs('mode1',level='Mode')
CFmode1 = CFmode1.unstack()
CFALLmode1 = DataMatrix.loc[('Kp'),('CFALL')].xs('mode1',level='Mode')
CFALLmode1 = CFALLmode1.unstack()
Rmode1 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode1',level='Mode')
Rmode1 = Rmode1.unstack()
RALLmode1 = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode1',level='Mode')
RALLmode1 = RALLmode1.unstack()
r11 = p.triangle( CFALLmode1['lung'] , RALLmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r12 = p.square( CFmode1['liver'] , Rmode1['liver'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r13 = p.circle( CFmode1['lung'] , Rmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
#------------
CFmode2 = DataMatrix.loc[('Kp'),('CF')].xs('mode2',level='Mode')
CFmode2 = CFmode2.unstack()
CFALLmode2 = DataMatrix.loc[('Kp'),('CFALL')].xs('mode2',level='Mode')
CFALLmode2 = CFALLmode2.unstack()
Rmode2 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode2',level='Mode')
Rmode2 = Rmode2.unstack()
RALLmode2 = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode2',level='Mode')
RALLmode2 = RALLmode2.unstack()
r21 = p.triangle( CFALLmode2['lung'] , RALLmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r22 = p.square( CFmode2['liver'] , Rmode2['liver'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r23 = p.circle( CFmode2['lung'] , Rmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
#------------
CFmode3 = DataMatrix.loc[('Kp'),('CF')].xs('mode3',level='Mode')
CFmode3 = CFmode3.unstack()
CFALLmode3 = DataMatrix.loc[('Kp'),('CFALL')].xs('mode3',level='Mode')
CFALLmode3 = CFALLmode3.unstack()
Rmode3 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode3',level='Mode')
Rmode3 = Rmode3.unstack()
RALLmode3 = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode3',level='Mode')
RALLmode3 = RALLmode3.unstack()
r31 = p.triangle( CFALLmode3['lung'] , RALLmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r32 = p.square( CFmode3['liver'] , Rmode3['liver'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r33 = p.circle( CFmode3['lung'] , Rmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
#------------
CFmode4 = DataMatrix.loc[('Kp'),('CF')].xs('mode4',level='Mode')
CFmode4 = CFmode4.unstack()
CFALLmode4 = DataMatrix.loc[('Kp'),('CFALL')].xs('mode4',level='Mode')
CFALLmode4 = CFALLmode4.unstack()
Rmode4 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode4',level='Mode')
Rmode4 = Rmode4.unstack()
RALLmode4 = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode4',level='Mode')
RALLmode4 = RALLmode4.unstack()
r41 = p.triangle( CFALLmode4['lung'] , RALLmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r42 = p.square( CFmode4['liver'] , Rmode4['liver'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r43 = p.circle( CFmode4['lung'] , Rmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
#------------
CFmode5 = DataMatrix.loc[('Kp'),('CF')].xs('mode5',level='Mode')
CFmode5 = CFmode5.unstack()
CFALLmode5 = DataMatrix.loc[('Kp'),('CFALL')].xs('mode5',level='Mode')
CFALLmode5 = CFALLmode5.unstack()
Rmode5 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode5',level='Mode')
Rmode5 = Rmode5.unstack()
RALLmode5 = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode5',level='Mode')
RALLmode5 = RALLmode5.unstack()
r51 = p.triangle( CFALLmode5['lung'] , RALLmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r52 = p.square( CFmode5['liver'] , Rmode5['liver'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r53 = p.circle( CFmode5['lung'] , Rmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
legend = Legend(items = [
("Mode1 - All Organs", [r11]),
("Mode1 - Liver", [r12]),
("Mode1 - Lung", [r13]),
("Mode2 - All Organs", [r21]),
("Mode2 - Liver", [r22]),
("Mode2 - Lung", [r23]),
("Mode3 - All Organs", [r31]),
("Mode3 - Liver", [r32]),
("Mode3 - Lung", [r33]),
("Mode4 - All Organs", [r41]),
("Mode4 - Liver", [r42]),
("Mode4 - Lung", [r43]),
("Mode5 - All Organs", [r51]),
("Mode5 - Liver", [r52]),
("Mode5 - Lung", [r53])], location = (0, -30))
p.add_layout(legend, "right")
p.legend.click_policy="mute"
p.xaxis.axis_label = 'CF'
p.yaxis.axis_label = 'R2'
show(p)
export_png(p, filename="KpCFR2single.png")
Out[57]:
R2 vs CF for Kp Miltiple plot:¶
In [58]:
#------------
p = figure(title="R2 vs CF ", plot_width=900, x_range = (0, 1), y_range = (0, 1)) #
#------------
CFmode1 = DataMatrix.loc[('Kp'),('CF')].xs('mode1',level='Mode')
CFmode1 = CFmode1.unstack()
CFALLmode1 = DataMatrix.loc[('Kp'),('CFALL')].xs('mode1',level='Mode')
CFALLmode1 = CFALLmode1.unstack()
Rmode1 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode1',level='Mode')
Rmode1 = Rmode1.unstack()
RALLmode1 = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode1',level='Mode')
RALLmode1 = RALLmode1.unstack()
r11 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode1 - All")
r11.triangle( CFALLmode1['lung'] , RALLmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r12 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode1 - Liver")
r12.square( CFmode1['liver'] , Rmode1['liver'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
r13 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode1 - Lung")
r13.circle( CFmode1['lung'] , Rmode1['lung'], size=10, color="dodgerblue", muted_color = "dodgerblue", muted_alpha=0.1)
# ----------
CFmode2 = DataMatrix.loc[('Kp'),('CF')].xs('mode2',level='Mode')
CFmode2 = CFmode2.unstack()
CFALLmode2 = DataMatrix.loc[('Kp'),('CFALL')].xs('mode2',level='Mode')
CFALLmode2 = CFALLmode2.unstack()
Rmode2 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode2',level='Mode')
Rmode2 = Rmode2.unstack()
RALLmode2 = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode2',level='Mode')
RALLmode2 = RALLmode2.unstack()
r21 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode2 - All")
r21.triangle( CFALLmode2['lung'] , RALLmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r22 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode2 - Liver")
r22.square( CFmode2['liver'] , Rmode2['liver'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
r23 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode2 - Lung")
r23.circle( CFmode2['lung'] , Rmode2['lung'], size=10, color="crimson", muted_color = "crimson", muted_alpha=0.1)
# ----------
CFmode3 = DataMatrix.loc[('Kp'),('CF')].xs('mode3',level='Mode')
CFmode3 = CFmode3.unstack()
CFALLmode3 = DataMatrix.loc[('Kp'),('CFALL')].xs('mode3',level='Mode')
CFALLmode3 = CFALLmode3.unstack()
Rmode3 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode3',level='Mode')
Rmode3 = Rmode3.unstack()
RALLmode3 = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode3',level='Mode')
RALLmode3 = RALLmode3.unstack()
r31 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode3 - All")
r31.triangle( CFALLmode3['lung'] , RALLmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r32 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode3 - Liver")
r32.square( CFmode3['liver'] , Rmode3['liver'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
r33 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode3 - Lung")
r33.circle( CFmode3['lung'] , Rmode3['lung'], size=10, color="limegreen", muted_color = "limegreen", muted_alpha=0.1)
# ----------
CFmode4 = DataMatrix.loc[('Kp'),('CF')].xs('mode4',level='Mode')
CFmode4 = CFmode4.unstack()
CFALLmode4 = DataMatrix.loc[('Kp'),('CFALL')].xs('mode4',level='Mode')
CFALLmode4 = CFALLmode4.unstack()
Rmode4 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode4',level='Mode')
Rmode4 = Rmode4.unstack()
RALLmode4 = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode4',level='Mode')
RALLmode4 = RALLmode4.unstack()
r41 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode4 - All")
r41.triangle( CFALLmode4['lung'] , RALLmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r42 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode4 - Liver")
r42.square( CFmode4['liver'] , Rmode4['liver'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
r43 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode4 - Lung")
r43.circle( CFmode4['lung'] , Rmode4['lung'], size=10, color="darkviolet", muted_color = "darkviolet", muted_alpha=0.1)
# ----------
CFmode5 = DataMatrix.loc[('Kp'),('CF')].xs('mode5',level='Mode')
CFmode5 = CFmode5.unstack()
CFALLmode5 = DataMatrix.loc[('Kp'),('CFALL')].xs('mode5',level='Mode')
CFALLmode5 = CFALLmode5.unstack()
Rmode5 = DataMatrix.loc[('Kp'),('Rsqrt')].xs('mode5',level='Mode')
Rmode5 = Rmode5.unstack()
RALLmode5 = DataMatrix.loc[('Kp'),('RsqrtALL')].xs('mode5',level='Mode')
RALLmode5 = RALLmode5.unstack()
r51 = figure(x_range = (0, 1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode5 - All")
r51.triangle( CFALLmode5['lung'] , RALLmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r52 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode5 - Liver")
r52.square( CFmode5['liver'] , Rmode5['liver'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
r53 = figure(x_range = (0, 1.1), y_range = (0, 1), plot_width=300, plot_height=300, title="Mode5 - Lung")
r53.circle( CFmode5['lung'] , Rmode5['lung'], size=10, color="darkgray", muted_color = "darkgray", muted_alpha=0.1)
p = gridplot([[r11, r21, r31, r41, r51], #, r21, r31, r41, r51],
[r12, r22, r32, r42, r52], #, r22, r32, r42, r52],
[r13, r23, r33, r43, r53]]) #, r23, r33, r43, r53],
show(p)
export_png(p, filename="KpCFR2Multi.png")
Out[58]: